Image processor, image processing method, and imaging device

ABSTRACT

An image processor according to the present disclosure includes: an image segmentation processing section to generate a plurality of first map data on the basis of first image map data including a plurality of pixel values, the plurality of first map data having arrangement patterns of pixel values different from each other and including pixel values located at positions different from each other; an interpolation processing section to generate a plurality of second map data by determining a pixel value at a position where no pixel value is present in each of the plurality of first map data with use of interpolation processing; and a synthesis processing section to generate third map data by generating, on the basis of pixel values at positions corresponding to each other in the plurality of second map data, a pixel value at a position corresponding to the positions.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a national stage application under 35 U.S.C. 371 andclaims the benefit of PCT Application No. PCT/JP2019/002984 having aninternational filing date of 29 Jan. 2019, which designated the UnitedStates, which PCT application claimed the benefit of Japanese PatentApplication No. 2018-022143 filed 9 Feb. 2018, the entire disclosures ofeach of which are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to an image processor that performs imageprocessing, an image processing method, and an imaging device includingsuch an image processor.

BACKGROUND ART

In imaging devices, captured images are generated on the basis ofelectric signals converted by red, green, and blue photoelectricconverters. For example, PTL 1 discloses that red, green, and bluephotoelectric converters are stacked in one pixel region.

CITATION LIST Patent Literature

PTL 1: Japanese Unexamined Patent Application Publication No.2011-138927

SUMMARY OF THE INVENTION

Incidentally, in imaging devices, high image quality of captured imagesis desired, and further improvement in the image quality is expected.

It is desirable to provide an image processor, an image processingmethod, and an imaging device that make it possible to enhance imagequality of a captured image.

An image processor according to an embodiment of the present disclosureincludes an imaging segmentation processing section, an interpolationprocessing section, and a synthesis processing section. The imagesegmentation processing section is configured to generate a plurality offirst map data on the basis of first image map data including aplurality of pixel values. The plurality of first map data hasarrangement patterns of pixel values different from each other andincludes pixel values located at positions different from each other.The interpolation processing section is configured to generate aplurality of second map data corresponding to the plurality of first mapdata by determining a pixel value at a position where no pixel value ispresent in each of the plurality of first map data with use ofinterpolation processing. The synthesis processing section is configuredto generate third map data by generating, on the basis of pixel valuesat positions corresponding to each other in the plurality of second mapdata, a pixel value at a position corresponding to the positions.

An image processing method according to an embodiment of the presentdisclosure includes: image segmentation processing of generating aplurality of first map data on the basis of first image map dataincluding a plurality of pixel values, the plurality of first map datahaving arrangement patterns of pixel values different from each otherand including pixel values located at positions different from eachother; interpolation processing of generating a plurality of second mapdata corresponding to the plurality of first map data by determining apixel value at a position where no pixel value is present in each of theplurality of first map data with use of interpolation processing; andsynthesis processing of generating third map data by generating, on thebasis of pixel values at positions corresponding to each other in theplurality of second map data, a pixel value at a position correspondingto the positions.

An imaging device according to an embodiment of the present disclosureincludes an imaging section, an imaging segmentation processing section,an interpolation processing section, and a synthesis processing section.The imaging section generates first image map data including a pluralityof pixel values. The image segmentation processing section is configuredto generate a plurality of first map data on the basis of the firstimage map data. The plurality of first map data has arrangement patternsof pixel values different from each other and includes pixel valueslocated at positions different from each other. The interpolationprocessing section is configured to generate a plurality of second mapdata corresponding to the plurality of first map data by determining apixel value at a position where no pixel value is present in each of theplurality of first map data with use of interpolation processing. Thesynthesis processing section is configured to generate third map data bygenerating, on the basis of pixel values at positions corresponding toeach other in the plurality of second map data, a pixel value at aposition corresponding to the positions.

The “imaging device” here is not limited to a so-called image sensoralone, and includes electronic devices having an imaging function suchas a digital camera and a smartphone.

In the image processor, the imaging processing method, and the imagingdevice according to the embodiments of the present disclosure, theplurality of first map data is generated on the basis of the first imagemap data by image segmentation processing. The plurality of first mapdata has arrangement patterns of pixel values different from each other,and includes pixel values located at positions different from eachother. Then, the plurality of second map data is generated on the basisof each of the plurality of first map data by interpolation processing.The plurality of second map data is generated by determining a pixelvalue at a position where no pixel value is present in the plurality offirst map data with use of interpolation processing. Then, the third mapdata is generated on the basis of the plurality of second map data bysynthesis processing. The third map data is generated by generating, onthe basis of pixel values at positions corresponding to each other inthe plurality of second map data, a pixel value at a positioncorresponding to the positions.

According to the image processor, the imaging processing method, and theimaging device according to the embodiments of the present disclosure,the plurality of first map data having arrangement patterns of pixelvalues different from each other and including pixel values located atpositions different from each other is generated on the basis of thefirst image map data, the plurality of second map data is generated bydetermining a pixel value at a position where no pixel value is presentin each of the plurality of first map data with use of interpolationprocessing, and the third map data is generated by generating, on thebasis of pixel values at positions corresponding to each other in theplurality of second map data, a pixel value at a position correspondingto the positions, which makes it possible to enhance image quality of acaptured image. It is to be noted that the effects described here arenot necessarily limited, but any of effects described in the presentdisclosure may be included.

BRIEF DESCRIPTION OF DRAWING

FIG. 1 is a block diagram illustrating a configuration example of animaging device according to a first embodiment of the presentdisclosure.

FIG. 2 is a block diagram illustrating a configuration example of animaging section illustrated in FIG. 1 .

FIG. 3 is an explanatory diagram illustrating a configuration example ofimaging pixels illustrated in FIG. 2 .

FIG. 4 is a schematic diagram illustrating a configuration example ofthe imaging pixels illustrated in FIG. 2 .

FIG. 5 is a flow chart illustrating an operation example of an imageprocessing section illustrated in FIG. 1 .

FIG. 6 is an explanatory diagram illustrating an operation example ofthe image processing section illustrated in FIG. 1 .

FIG. 7 is an explanatory diagram illustrating an example of image mapdata illustrated in FIG. 6 .

FIG. 8A is an explanatory diagram illustrating an example of map dataillustrated in FIG. 6

FIG. 8B is another explanatory diagram illustrating an example of themap data illustrated in FIG. 6 .

FIG. 9A is another explanatory diagram illustrating an example of themap data illustrated in FIG. 6 .

FIG. 9B is another explanatory diagram illustrating an example of themap data illustrated in FIG. 6 .

FIG. 10 is another explanatory diagram illustrating an example of themap data illustrated in FIG. 6 .

FIG. 11 is an explanatory diagram illustrating an operation example ofan image processing section according to a modification example.

FIG. 12A is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 12B is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 13A is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 13B is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 14A is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 14B is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 15A is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 15B is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 16A is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 16B is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 17A is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 17B is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 18A is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 18B is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 18C is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 19A is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 19B is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 19C is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 20 is another explanatory diagram illustrating an example of mapdata according to another modification example.

FIG. 21 is a block diagram illustrating a configuration example of animaging device according to another modification example.

FIG. 22 is an explanatory diagram illustrating an operation example ofan image processing section illustrated in FIG. 21 .

FIG. 23A is an explanatory diagram illustrating an operation example ofthe image processing section illustrated in FIG. 21 .

FIG. 23B is an explanatory diagram illustrating an operation example ofthe image processing section illustrated in FIG. 21 .

FIG. 23C is an explanatory diagram illustrating an operation example ofthe image processing section illustrated in FIG. 21 .

FIG. 24 is an explanatory diagram illustrating an operation example ofan image processing section according to another modification example.

FIG. 25 is a block diagram illustrating a configuration example of animaging device according to another modification example.

FIG. 26 is an explanatory diagram illustrating an operation example ofan image processing section illustrated in FIG. 25 .

FIG. 27 is a block diagram illustrating a configuration example of animaging device according to a second embodiment.

FIG. 28 is an explanatory diagram illustrating a configuration exampleof imaging pixels in an imaging section illustrated in FIG. 27 .

FIG. 29 is a schematic diagram illustrating a configuration example ofthe imaging pixels in the imaging section illustrated in FIG. 27 .

FIG. 30 is an explanatory diagram illustrating an operation example ofan image processing section illustrated in FIG. 27 .

FIG. 31 is a block diagram illustrating a configuration example of animaging device according to a third embodiment.

FIG. 32 is an explanatory diagram illustrating a configuration exampleof imaging pixels in an imaging section illustrated in FIG. 31 .

FIG. 33 is a schematic diagram illustrating a configuration example ofthe imaging pixels in the imaging section illustrated in FIG. 31 .

FIG. 34 is an explanatory diagram illustrating an operation example ofan image processing section illustrated in FIG. 31 .

FIG. 35 is a block diagram illustrating a configuration example of animaging device according to a modification example.

FIG. 36 is an explanatory diagram illustrating an operation example ofan image processing section illustrated in FIG. 35 .

FIG. 37 is a block diagram illustrating a configuration example of animaging device according to a fourth embodiment.

FIG. 38 is an explanatory diagram illustrating a configuration exampleof imaging pixels in an imaging section illustrated in FIG. 37 .

FIG. 39 is a schematic diagram illustrating a configuration example ofthe imaging pixels in the imaging section illustrated in FIG. 37 .

FIG. 40 is an explanatory diagram illustrating an operation example ofan image processing section illustrated in FIG. 37 .

FIG. 41 is an explanatory diagram illustrating usage examples of animaging device.

FIG. 42 is a block diagram depicting an example of a schematicconfiguration of an in-vivo information acquisition system.

FIG. 43 is a view depicting an example of a schematic configuration ofan endoscopic surgery system.

FIG. 44 is a block diagram depicting an example of a functionalconfiguration of a camera head and a camera control unit (CCU).

FIG. 45 is a block diagram depicting an example of schematicconfiguration of a vehicle control system.

FIG. 46 is a diagram of assistance in explaining an example ofinstallation positions of an outside-vehicle information detectingsection and an imaging section.

MODES FOR CARRYING OUT THE INVENTION

The following describes embodiments of the present disclosure in detailwith reference to the drawings. It is to be noted that description isgiven in the following order.

1. First Embodiment

2. Second Embodiment

3. Third Embodiment

4. Fourth Embodiment

3. Usage Examples of Imaging Device

6. Application Examples

1. First Embodiment Configuration Example

FIG. 1 illustrates a configuration example of an imaging device 1including an image processor according to a first embodiment. It is tobe noted that an image processing method according to an embodiment ofthe present embodiment is embodied by the present embodiment, and isdescribed together. The imaging device 1 includes an optical system 9,an imaging section 10, and an image processing section 20.

The optical system 9 includes, for example, a lens that forms an imageon an imaging surface S of the imaging section 10.

The imaging section 10 captures an image of a subject to generate animage signal DT and a gain signal SGAIN. The imaging section 10 isconfigured using, for example, a CMOS (complementary metal oxidesemiconductor) image sensor.

FIG. 2 illustrates a configuration example of the imaging section 10.The imaging section 10 includes a pixel array 11, a scanning section 12,a readout section 13, and an imaging controller 14.

The pixel array 11 includes a plurality of imaging pixels P arranged ina matrix. The imaging pixels P each include a photoelectric converterthat is configured to receive red (R) light, a photoelectric converterthat is configured to receive green (G) light, and a photoelectricconverter that is configured to receive blue (B) light.

FIG. 3 schematically illustrates cross-sectional configurations of theimaging pixels P. FIG. 3 schematically illustrates cross-sectionalconfigurations of two imaging pixels P of four imaging pixels P arrangedin a region X illustrated in FIG. 2 .

A semiconductor substrate 100 includes two photodiodes PDR and PDBformed in a pixel region corresponding to one imaging pixel P. Thephotodiode PDR is a photoelectric converter that is configured toreceive red (R) light, and the photodiode PDB is a photoelectricconverter that is configured to receive blue (B) light. The photodiodePDR and the photodiode PDB are formed and stacked in the semiconductorsubstrate 100 in such a manner that the photodiode PDB is located onside of the imaging surface S. The photodiode PDR and the photodiode PDBrespectively perform photoelectric conversion on the basis of red lightand blue light with use of a fact that an absorption coefficient oflight in the semiconductor substrate 100 differs depending on awavelength of the light.

An insulating film 101 is formed on a surface, on the side of theimaging surface S, of the semiconductor substrate 100. The insulatingfilm 101 is configured using, for example, silicon dioxide (SiO₂). Then,a transparent electrode 102, a photoelectric conversion film 103G, and atransparent electrode 104 are formed in this order on the insulatingfilm 101. The transparent electrodes 102 and 104 are electrodes thatallow red light, green light, and blue light to pass therethrough. Thephotoelectric conversion film 103G is a photoelectric conversion filmthat is configured to receive green (G) light, and allows red light andblue light to pass therethrough. The photoelectric conversion film 103Gand the transparent electrodes 102 and 104 are included in aphotoelectric converter that is configured to receive green (G) light.An on-chip lens 105 is formed on the transparent electrode 104.

FIG. 4 schematically illustrates positions of photoelectric convertersin the four imaging pixels P arranged in the region X illustrated inFIG. 2 . In the imaging section 10, a photoelectric converter related togreen (G), a photoelectric converter related to blue (B), and aphotoelectric converter related to red (R) are formed and stacked insuch a manner in the pixel region corresponding to one imaging pixel P.This makes it possible for each of the imaging pixels P to generate apixel signal related to red, a pixel signal related to green, and apixel signal related to blue in the imaging section 10.

The scanning section 12 sequentially drives the plurality of imagingpixels P in the pixel array 11, for example, in units of pixel lines onthe basis of an instruction from the imaging controller 14, andincludes, for example, an address decoder.

The readout section 13 performs AD conversion on the basis of the pixelsignals supplied from the respective imaging pixels P on the basis of aninstruction from the imaging controller 14 to generate an image signalDT. The image signal DT includes three image map data MPG, MPB, and MPR.The image map data MPG includes pixel values for one frame image relatedto green (G). The image map data MPB includes pixel values for one frameimage related to blue (B). The image map data MPR includes pixel valuesfor one frame image related to red (R). Each of the pixel values isrepresented by a digital code having a plurality of bits.

The imaging controller 14 supplies a control signal to the scanningsection 12 and the readout section 13 to control operations of thesecircuits, thereby controlling an operation of the imaging section 10. Inaddition, the imaging controller 14 also has a function of setting aconversion gain GC for AD conversion to be performed by the readoutsection 13. Specifically, in a case where the imaging section 10captures an image of a dark subject, the imaging controller 14 increasesthe conversion gain GC for AD conversion to be performed, and in a casewhere the imaging section 10 captures an image of a bright subject, theimaging controller 14 decreases the conversion gain GC for AD conversionto be performed. This makes it possible for the imaging device 1 tocapture images of subjects having various levels of brightness. Inaddition, the imaging controller 14 also has a function of outputtinginformation about this conversion gain GC as the gain signal SGAIN.

The image processing section 20 (FIG. 1 ) performs image processing onthe basis of the image signal DT and the gain signal SGAIN. The imageprocessing section 20 includes a switching section 21, an imagesegmentation processing section 22, an interpolation processing section23, a synthesis processing section 24, and a signal processing section25.

The switching section 21 selectively supplies the image signal DT to theimage segmentation processing section 22 or the signal processingsection 25 on the basis of the conversion gain GC indicated by the gainsignal SGAIN. Specifically, for example, the switching section 21supplies the image signal DT to the image segmentation processingsection 22 in a case where the conversion gain GC is higher than apredetermined threshold value Gth, and supplies the image signal DT tothe signal processing section 25 in a case where the conversion gain GCis lower than the predetermined threshold value Gth. This makes itpossible to cause the image segmentation processing section 22, theinterpolation processing section 23, and the synthesis processingsection 24 in the image processing section 20 to perform processing inthe case where the conversion gain GC is higher than the predeterminedthreshold value Gth, and makes it possible to cause the imagesegmentation processing section 22, the interpolation processing section23, and the synthesis processing section 24 in the image processingsection 20 not to perform processing in the case where the conversiongain GC is lower than the predetermined threshold value Gth.

The image segmentation processing section 22 performs image segmentationprocessing A1 on the basis of the three image map data MPG, MPB, and MPRincluded in the image signal DT supplied from the imaging section 10 viathe switching section 21 to generate six map data MG11, MG12, MB11,MB12, MR11, and MR12. Specifically, the image segmentation processingsection 22 generates, on the basis of the image map data MPG related togreen (G) included in the image signal DT, two map data MG11 and MG12that have arrangement patterns PAT of pixel values different from eachother and include pixel values located at positions different from eachother, as described later. Similarly, the image segmentation processingsection 22 generates two map data MB11 and MB12 on the basis of theimage map data MPB related to blue (B) included in the image signal DT,and generates two map data MR11 and MR12 on the basis of the image mapdata MPR related to red (R) included in the image signal DT. Thus, theimage segmentation processing section 22 generates the six map dataMG11, MG12, MB11, MB12, MR11, and MR12 on the basis of the image signalDT.

The interpolation processing section 23 respectively performsinterpolation processing A2 on the six map data MG11, MG12, MB11, MB12,MR11, and MR12 supplied from the image segmentation processing section22 to generate six map data MG21, MG22, MB21, MB22, MR21, and MR22.Specifically, as described later, the interpolation processing section23 determines a pixel value at a position where no pixel value ispresent in the map data MG11 related to green (G) with use of theinterpolation processing A2 to generate the map data MG21, anddetermines a pixel value at a position where no pixel value is presentin the map data MG12 related to the green (G) with use of theinterpolation processing A2 to generate map data MG22. Similarly, theinterpolation processing section 23 performs the interpolationprocessing A2 on the map data MB11 related to blue (B) to generate themap data MB21, and performs the interpolation processing A2 on the mapdata MB12 related to blue (B) to generate the map data MB22. Inaddition, the interpolation processing section 23 performs theinterpolation processing A2 on the map data MR11 related to the red (R)to generate the map data MR21, and performs the interpolation processingA2 on the map data MR12 related to the red (R) to generate the map dataMR22.

The synthesis processing section 24 performs synthesis processing A3 onthe basis of the six map data MG21, MG22, MB21, MB22, MR21, and MR22supplied from the interpolation processing section 23 to generate threemap data MG3, MB3, and MR3. Specifically, the synthesis processingsection 24 generates the map data MG3 on the basis of the two map dataMG21 and MG22 related to green (G), as described later. Similarly, thesynthesis processing section 24 generates the map data MB3 on the basisof the two map data MB21 and MB22 related to blue (B), and generates, onthe basis of pixel values at positions corresponding to each other inthe two map data MR21 and MR22 related to red (R), a pixel value at aposition corresponding to the positions to generate the map data MR3.Then, the synthesis processing section 24 supplies the three map dataMG3, MB3, and MR3 as an image signal DT2 to the signal processingsection 25.

The signal processing section 25 performs predetermined signalprocessing on the basis of the image signal DT2 supplied from thesynthesis processing section 24 or the image signal DT supplied from theimaging section 10 via the switching section 21. The predeterminedsignal processing includes, for example, white balance adjustment,nonlinear conversion, contour enhancement processing, image sizeconversion, and the like. Then, the signal processing section 25 outputsa processing result of the predetermined signal processing as an imagesignal DT3.

With this configuration, in a case where an image of a dark subject iscaptured, the conversion gain GC is increased in the imaging device 1;therefore, the image segmentation processing A1, the interpolationprocessing A2, and the synthesis processing A3 are performed. This makesit possible to increase a signal-to-noise ratio (S/N ratio) in thecaptured image in the imaging device 1. In addition, in a case where theimaging device 1 captures an image of a bright subject, the conversiongain GC is decreased in the imaging device 1; therefore, the imagesegmentation processing A1, the interpolation processing A2, and thesynthesis processing A3 are not performed. This makes it possible toincrease resolution in the captured image in the imaging device 1.

Here, the image processing section 20 corresponds to a specific exampleof an “image processor” in the present disclosure. The imagesegmentation processing section 22 corresponds to a specific example ofan “image segmentation processing section” in the present disclosure.The interpolation processing section 23 corresponds to a specificexample of an “interpolation processing section” in the presentdisclosure. The synthesis processing section 24 corresponds to aspecific example of a “synthesis processing section” in the presentdisclosure. The signal processing section 25 corresponds to a specificexample of a “processing section” in the present disclosure. Theswitching section 21 corresponds to a specific example of a “processingcontroller” in the present disclosure.

[Operation and Workings]

Next, description is given of an operation and workings of the imagingdevice 1 according to the present embodiment.

(Overview of Overall Operation)

First, description is given of an overview of an overall operation ofthe imaging device 1 with reference to FIG. 1 . The imaging section 10captures an image of a subject to generate the image signal DT and thegain signal SGAIN. The switching section 21 of the image processingsection 20 selectively supplies the image signal DT to the imagesegmentation processing section 22 or the signal processing section 25on the basis of the conversion gain GC indicated by the gain signalSGAIN. The image segmentation processing section 22 performs the imagesegmentation processing A1 on the basis of three image map data MPG,MPB, and MPR included in the image signal DT supplied from the imagingsection 10 via the switching section 21 to generate six map data MG11,MG12, MB11, MB12, MR11, and MR12. The interpolation processing section23 respectively performs the interpolation processing A2 on the six mapdata MG11, MG12, MB11, MB12, MR11, and MR12 supplied from the imagesegmentation processing section 22 to generate six map data MG21, MG22,MB21, MB22, MR21, and MR22. The synthesis processing section 24 performsthe synthesis processing A3 on the basis of the six map data MG21, MG22,MB21, MB22, MR21, and MR22 supplied from the interpolation processingsection 23 to generate three map data MG3, MB3, and MR3. Then, thesynthesis processing section 24 supplies these three map data MG3, MB3,and MR3 as the image signal DT2 to the signal processing section 25. Thesignal processing section 25 performs the predetermined signalprocessing on the basis of the image signal DT2 supplied from thesynthesis processing section 24 or the image signal DT supplied from theimaging section 10 via the switching section 21 to generate the imagesignal DT3.

(Detailed Operation)

FIG. 5 illustrates an operation example of the image processing section20. The image processing section 20 determines whether or not to performthe image segmentation processing A1, the interpolation processing A2,and the synthesis processing A3 on the basis of the conversion gain GCindicated by the gain signal SGAIN. This operation is described indetail below.

First, the switching section 21 compares the conversion gain GCindicated by the gain signal SGAIN with the predetermined thresholdvalue Gth (step S101). In a case where the conversion gain GC is lowerthan the predetermined threshold value Gth (“N” in the step S101), theprocessing proceeds to step S105.

In a case where the conversion gain GC is equal to or higher than thepredetermined threshold value Gth (G Gth) (“Y” in the step S101), theimage segmentation processing section 22 performs the image segmentationprocessing A1 (step S102), the interpolation processing section 23performs the interpolation processing A2 (step S103), and the synthesisprocessing section 24 performs the synthesis processing A3 (step S104).

Then, the signal processing section 25 performs the predetermined signalprocessing (step S105). That is, the signal processing section 25performs the predetermined signal processing on the basis of the imagesignal DT2 generated by the synthesis processing A3 in the case wherethe conversion gain GC is equal to or higher than the predeterminedthreshold value Gth (“Y” in the step S101), and performs thepredetermined signal processing on the basis of the image signal DTgenerated by the imaging section 10 in the case where the conversiongain GC is lower than the predetermined threshold value Gth (“N” in thestep S101).

Thus, this flow ends.

As described above, the image processing section 20 performs the imagesegmentation processing A1, the interpolation processing A2, and thesynthesis processing A3 in a case where the conversion gain GC is high.In addition, in a case where the conversion gain GC is low, the imagesegmentation processing A1, the interpolation processing A2, and thesynthesis processing A3 are not performed in the imaging device 1. Thismakes it possible to enhance image quality of a captured image in theimaging device 1, as described below.

Next, the image segmentation processing A1, the interpolation processingA2, and the synthesis processing A3 are described in detail withspecific operation examples.

FIG. 6 schematically illustrates examples of the image segmentationprocessing A1, the interpolation processing A2, and the synthesisprocessing A3 in the image processing section 20.

(Image Segmentation Processing A1)

The image segmentation processing section 22 performs the imagesegmentation processing A1 on the basis of three image map data MPG,MPB, and MPR included in the image signal DT supplied from the imagingsection 10 to generate six map data MG11, MG12, MB11, MB12, MR11, andMR12. The image segmentation processing A1 on the image map data MPGrelated to green (G) is described in detail as an example below.

FIG. 7 schematically illustrates the image map data MPG related to green(G). FIGS. 8A and 8B schematically illustrate the map data MG11 and MG12related to green (G), respectively. In FIGS. 8A and 8B, a shaded portionindicates a position where a pixel value is present, and an unshadedportion indicates a position where a pixel value is not present (nopixel value is present).

The image map data MPG (FIG. 7 ) included in the image signal DTincludes pixel values in one frame image related to green (G). Anexample of the image map data MPG illustrated in FIG. 6 schematicallyillustrates four pixel values arranged in two rows and two columns inthe region X illustrated in FIG. 7 .

The image segmentation processing section 22 generates, on the basis ofsuch image map data MPG, two map data MG11 and MG12 (FIGS. 8A and 8B)that have arrangement patterns PAT of pixel values different from eachother and include pixel values located at positions different from eachother. The arrangement patterns PAT of the pixel values in the map dataMG11 and MG12 are checkered patterns (Checkered Patterns) in which pixelvalues are shifted by one pixel in a horizontal direction (a lateraldirection) and a vertical direction (a longitudinal direction) to eachother. In other words, in the checkered patterns in the map data MG11and MG12, positions where pixel values are present and positions whereno pixel value is present are reversed from each other, and the pixelvalues are arranged at positions different from each other.Specifically, for example, in the map data MG11, as illustrated in FIG.8A, the pixel values are present on the upper left and the lower rightin the region X, and no pixel value is present on the lower left and theupper right in the region X. In contrast, in the map data MG12, asillustrated in FIG. 8B, the pixel values are present on the lower leftand the upper right in the region X, and no pixel value is present onthe upper left and the lower right in the region X. An example of eachof the map data MG11 and MG12 illustrated in FIG. 6 schematicallyillustrates four pixel values in this region X. The pixel value at eachposition in the map data MG11 is the same as the pixel value at acorresponding position in the image map data MPG. Similarly, the pixelvalue at each position in the map data MG12 is the same as the pixelvalue at a corresponding position in the image map data MPG.

The image segmentation processing section 22 performs the imagesegmentation processing A1 on the basis of the image map data MPG togenerate such map data MG11 and MG12. Similarly, the image segmentationprocessing section 22 performs the image segmentation processing A1 onthe basis of the image map data MPB to generate the map data MB11 andMB12, and performs the image segmentation processing A1 on the basis ofthe image map data MPR to generate the map data MR11 and MR12. Asillustrated in FIG. 6 , the map data MG11, MB11, and MR11 have the samearrangement pattern PAT, and the map data MG12, MB12, and MR12 have thesame arrangement pattern PAT.

(Interpolation Processing A2)

Next, the interpolation processing section 23 respectively performs theinterpolation processing A2 on the six map data MG11, MR12, MB11, MB12,MR11, and MR12 generated by the image segmentation processing A1 togenerate six map data MG21, MG22, MB21, MB22, MR21, and MR22. Theinterpolation processing A2 on the map data MG11 and MG12 (FIGS. 8A and8B) related to green (G) is described in detail as an example below.

FIGS. 9A and 9B schematically illustrate the map data MG21 and MG22related to green (G), respectively. In FIGS. 9A and 9B, a shaded portionindicates a position where a pixel value is present in the map data MG11and MG12 before the interpolation processing A2, and an unshaded portionindicates a position where a pixel value is not present in the map dataMG11 and MG12 before the interpolation processing A2 and a positionwhere a pixel value is generated by this interpolation processing A2.

The interpolation processing section 23 determines a pixel value at aposition where no pixel value is present in the map data MG11illustrated in FIG. 8A with use of the interpolation processing A2 togenerate the map data MG21 illustrated in FIG. 9A, and determines apixel value at a position where no pixel value is present in the mapdata MG12 illustrated in FIG. 8B with use of the interpolationprocessing A2 to generate the map data MG22 illustrated in FIG. 9B.Specifically, the interpolation processing section 23 determines a pixelvalue at a position where no pixel value is present by performing theinterpolation processing A2 on the basis of pixel values located one rowabove, one column to the left of, one row below, and one column to theright of the position where no pixel value is present. That is, aninterpolation method in the interpolation processing A2 in this exampleuses the pixel value above, below, to the left of, and to the right ofthe position where no pixel value is present. In the interpolationprocessing section 23, for example, performing bilinear interpolationwith use of these four pixel values makes it possible to perform theinterpolation processing A2. It is to be noted that, without limiting tothis interpolation method, it is possible to use various knowninterpolation methods such as bicubic interpolation and splineinterpolation. For example, in the map data MG21, as illustrated in FIG.9A, the interpolation processing section 23 generates a pixel value at alower left position in the region X by the interpolation processing A2,and generates a pixel value at an upper right position in the region Xby the interpolation processing A2. Similarly, in the map data MG22, asillustrated in FIG. 9B, the interpolation processing section 23generates a pixel value at an upper left position in the region X by theinterpolation processing A2, and generates a pixel value at a lowerright position in the region X by the interpolation processing A2. InFIGS. 9A and 9B, “G” indicates that a pixel value has been generated bythe interpolation processing A2. An example of each of the map data MG21and MG22 illustrated in FIG. 6 schematically illustrates four pixelvalues in this region X.

The interpolation processing section 23 performs the interpolationprocessing A2 on the map data MG11 to generate such map data MG21, andperforms the interpolation processing A2 on the map data MG12 togenerate such map data MG22. Similarly, the interpolation processingsection 23 performs the interpolation processing A2 on the map data MB11to generate the map data MB21, and performs the interpolation processingA2 on the map data MB12 to generate the map data MB22. In addition, theinterpolation processing section 23 performs the interpolationprocessing A2 on the map data MR11 to generate the map data MR21, andperforms the interpolation processing A2 on the map data MR12 togenerate the map data MR22. The six map data MG21, MG22, MB21, MB22,MR21, and MR22 are generated by the same interpolation method.

(Synthesis Processing A3)

Next, the synthesis processing section 24 performs the synthesisprocessing A3 on the basis of the six map data MG21, MG22, MB21, MB22,MR21, and MR22 generated by the interpolation processing A2 to generatethree map data MG3, MB3, and MR3. The synthesis processing A3 on the mapdata MG21 and MG22 (FIGS. 9A and 9B) related to green (G) is describedin detail as an example below.

FIG. 10 schematically illustrates the map data MG3 related to green (G).The synthesis processing section 24 generates, on the basis of the pixelvalues at the positions corresponding to each other in two map data MG21and MG22, a pixel value at a position corresponding to the positions togenerate the map data MG3. Specifically, it is possible for thesynthesis processing section 24 to generate a pixel value at theposition in map data MG3 by summing the pixel values at the positionscorresponding to each other in the two map data MG21 and MG22. Forexample, the synthesis processing section 24 generates a pixel value onthe upper left in the region X of the map data MG3 illustrated in FIG.10 by summing a pixel value on the upper left in the region X of the mapdata MG21 illustrated in FIG. 9A and a pixel value on the upper left inthe region X of the map data MG22 illustrated in FIG. 9B. Similarly, thesynthesis processing section 24 generates a pixel value on the lowerleft in the region X of the pixel value by summing pixel values on thelower left in the regions X of the map data MG1 and MG2, generates apixel value on the upper right in the region X of the map data MG3 bysumming pixel values on the upper right in the regions X of the map dataMG21 and MG22, and generates a pixel value on the lower right in theregion of the map data MG3 by summing pixel values on the lower right inthe regions X of the map data MG21 and MG22. In FIG. 10 , “2G” indicatesthat a pixel value becomes about twice the pixel value in the image mapdata MPG by the synthesis processing A3. An example of the map data MG3illustrated in FIG. 6 schematically illustrates pixel values in thisregion X.

The synthesis processing section 24 performs the synthesis processing A3on the basis of the map data MG21 and MG22 to generates such map dataMG3. Similarly, the synthesis processing section 24 performs thesynthesis processing A3 on the basis of the map data MB21 and MB22 togenerate the map data MB3, and performs the synthesis processing A3 onthe basis of the map data MR21 and MR22 to generate the map data MR3.The pixel values in the map data MB3 are about twice the pixel values inthe image map data MPB, and the pixel values in the map data MR3 areabout twice the pixel values in the image map data MPR.

As described above, the synthesis processing section 24 generates threemap data MG3, MB3, and MR3. Then, the synthesis processing section 24supplies the three map data MG3, MB3, and MR3 as the image signal DT2 tothe signal processing section 25.

Here, the image map data MPG, MPB, and MPR respectively correspond tospecific examples of “first image map data”, “second image map data”,and “third image map data” in the present disclosure. The map data MG11and MG12 correspond to a specific example of a “plurality of first mapdata” in the present disclosure. The map data MG21 and MG22 correspondto a specific example of a “plurality of second map data” in the presentdisclosure. The map data MG3 corresponds to a specific example of “thirdmap data” in the present disclosure. The map data MB11 and MB12correspond to a specific example of a “plurality of fourth map data” inthe present disclosure. The map data MB21 and MB22 correspond tospecific examples of “fifth map data” in the present disclosure. The mapdata MB3 corresponds to a specific example of “sixth map data” in thepresent disclosure. The map data MR11 and MR12 correspond to specificexamples of “seventh map data” in the present disclosure. The map dataMR21 and MR22 correspond to a specific example of a “plurality of eighthmap data” in the present disclosure. The map data MR3 corresponds to aspecific example of “ninth map data” in the present disclosure.

As described above, in the imaging device 1, for example, the imagesegmentation processing A1 is performed on the basis of the image mapdata MPG to generate the map data MG11 and MG12, the interpolationprocessing A2 is respectively performed on these map data MG11 and MG12to generate the map data MG21 and MG22, and the synthesis processing A3is performed on the basis of these map data MG21 and MG22 to generatethe map data MG3. The same applies to the image map data MPB and MPR.This makes it possible to increase a signal-to-noise ratio (SN ratio) inthe map data MG3, MB3, and MR3 in the imaging device 1.

That is, the synthesis processing section 24 determines the pixel valueon the upper left in the region X of the map data MG3, for example, bysumming the pixel value on the upper left in the region X of the mapdata MG21 and the pixel value on the upper left in the region X of themap data MG22. Each of the pixel values has a signal component and anoise component that is random noise. Accordingly, the synthesisprocessing section 24 sums the pixel value on the upper left in theregion X of the map data MG21 and the pixel value on the upper left inthe region X of the map data MG22 to increase the signal component by afactor of about two and increase the noise component by a factor ofabout 1.4. That is, the noise component is random noise as describedabove, and the noise component included in the pixel value on the upperleft in the region X of the map data MG21 and the noise componentincluded in the pixel value on the upper left in the region X of the mapdata MG22 are mutually independent noise components; therefore, thenoise component is not increased by a factor of about two but by afactor of about 1.4 (the square root of 2). Thus, in the imaging device1, the signal component is increased by a factor of about two and thenoise component is increased by a factor of about 1.4, which makes itpossible to increase the signal-to-noise ratio (S/N ratio) in the mapdata MG3. The same applies to the map data MB3 and MR3. Thisconsequently makes it possible to enhance image quality of a capturedimage in the imaging device 1.

In addition, in the imaging device 1, in the image segmentationprocessing A1, the arrangement patterns PAT of the pixel values arecheckered patterns. Accordingly, as illustrated in FIGS. 8A and 8B,pixel values are present above, below, to the left of, and to the rightof a position where no pixel value is present; therefore, performing theinterpolation processing A2 makes it possible to determine the pixelvalue at the position where no pixel value is present on the basis ofthese four pixel values. As described above, in the imaging device 1, itis possible to perform the interpolation processing A2 on the basis ofthe pixel values above, below, to the left, and to the right, whichmakes it possible to make a reduction in resolution in the horizontaldirection and a reduction in resolution in the vertical directionsubstantially equal to each other, and to suppress a reduction inresolution. This consequently makes it possible to enhance image qualityof a captured image in the imaging device 1.

Further, in the imaging device 1, in the image segmentation processingA1, the arrangement patterns PAT of the pixel values in the map dataMG11, MB11, and MR11 are the same as each other, and the arrangementpatterns PAT of the pixel values in the map data MG12, MB12, and MR12are the same as each other. This makes it possible for the imagesegmentation processing section 22 to perform the image segmentationprocessing A1 by the same method on the basis of three image map dataMPG, MPB, and MPR, which makes it possible to simplify a circuitconfiguration of the image segmentation processing section 22, ascompared with a case where the image segmentation processing A1 isperformed by different methods on the basis of the three image map dataMPG, MPB, and MPR.

Further, in the imaging device 1, in the interpolation processing A2,interpolation methods for generating the map data MG21, MG22, MB21,MB22, MR21, and MR22 are the same as each other. This makes it possiblefor the interpolation processing section 23 to generate six map dataMG21, MG22, MB21, MB22, MR21, and MR22 with use of the sameinterpolation method, which makes it possible to simplify a circuitconfiguration of the interpolation processing section 23, as comparedwith a case where six map data MG21, MG22, MB21, MB22, MR21, and MR22are generated with use of different interpolation methods.

Further, in the imaging device 1, in the image segmentation processingA1, the arrangement patterns PAT of the pixel values in the map dataMG11, MB11, and MR11 are the same as each other, and the arrangementpatterns PAT of the pixel values in the map data MG12, MB12, and MR12are the same as each other. Then, in the interpolation processing A2,the interpolating methods for generating six map data MG21, MG22, MB21,MB22, MR21, and MR22 are the same as each other. This makes it possibleto suppress false colors in a captured image in the imaging device 1.That is, for example, in a case where the interpolation processing A2 isperformed on the basis of the pixel values located one row above and onerow below the position where no pixel value is present to generate themap data MG21 and MG22 related to green (G) and the interpolationprocessing A2 is performed on the basis of the pixel values located onecolumn to the left of and one column to the right of the position whereno pixel value is present to generate the map data MB21 and MB22 relatedto blue (B), the interpolating method in the interpolation processing A2differs depending on colors, which may cause false colors locally. Incontrast, in the imaging device 1 according to the present embodiment,in the image segmentation processing A1, the arrangement patterns PAT ofthe pixel values in the map data MG11, MB11, and MR11 are the same aseach other, and the arrangement patterns PAT of the pixel values in themap data MG12, MB12, and MR12 are the same as each other. Then, in theinterpolation processing A2, the interpolating methods for generatingsix map data MG21, MG22, MB21, MB22, MR21, and MR22 are the same as eachother. This makes it possible to reduce a possibility that such falsecolors occur in the imaging device 1. This consequently makes itpossible to enhance image quality of a captured image in the imagingdevice 1.

Further, in the imaging device 1, it is possible to control whether ornot to perform image segmentation processing, interpolation processing,and synthesis processing, which makes it possible to enhance imagequality of a captured image. In particular, the imaging device 1controls whether or not to perform the image segmentation processing,the interpolation processing, and the synthesis processing on the basisof the conversion gain GC in the imaging section 10. Specifically, theimage segmentation processing, the interpolation processing, and thesynthesis processing are performed in a case where the conversion gainGC indicated by the gain signal SGAIN is higher than the predeterminedthreshold value Gth, and the image segmentation processing, theinterpolation processing, and the synthesis processing are not performedin a case where the conversion gain GC is lower than the predeterminedthreshold value Gth. Thus, for example, in a case where the imagingdevice 1 captures an image of a dark subject, the conversion gain GC isincreased; therefore, the image segmentation processing A1, theinterpolation processing A2, and the synthesis processing A3 areperformed. This makes it possible to increase a signal-to-noise ratio(S/N ratio) in the captured image in the imaging device 1. That is, inthe case where an image of a dark subject is captured, there is apossibility that noises are increased; therefore, performing the imagesegmentation processing A1, the interpolation processing A2, and thesynthesis processing A3 makes it possible to increase thesignal-to-noise ratio (S/N ratio) in the captured image. Further, in acase where the imaging device 1 captures an image of a bright subject,the conversion gain GC is decreased; therefore, the image segmentationprocessing A1, the interpolation processing A2, and the synthesisprocessing A3 are not performed. This makes it possible to increaseresolution in the captured image in the imaging device 1. That is, in acase where an image of a bright subject is captured, less noises aregenerated, not performing the image segmentation processing A1, theinterpolation processing A2, and the synthesis processing A3 makes itpossible to increase resolution. This consequently makes it possible toenhance image quality of the captured image in the imaging device 1.

[Effects]

A described above, in the present embodiment, the image segmentationprocessing, the interpolation processing, and the synthesis processingare performed, which makes it possible to increase the signal-to-noiseratio in the captured image. This makes it possible to enhance imagequality of the captured image.

In the present embodiment, in the image segmentation processing, thearrangement patterns of the pixel values are checkered patterns, whichmakes it possible to make a reduction in resolution in the horizontaldirection and a reduction in resolution in the vertical directionsubstantially equal to each other, and suppress a reduction inresolution. This makes it possible to enhance image quality of thecaptured image.

In the present embodiment, in the image segmentation processing, thearrangement patterns of the pixel values in the map data MG11, MB11, andMR11 are the same as each other, and the arrangement patterns of thepixel values in the map data MG12, MB12, and MR12 are the same as eachother, which makes it possible to simplify a circuit configuration ofthe image segmentation processing section.

In the present embodiment, the interpolating methods for generating sixmap data in the interpolation processing are the same as each other,which makes it possible to simplify a circuit configuration of theinterpolation processing section.

In the present embodiment, in the image segmentation processing, thearrangement patterns of the pixel values in the map data MG11, MB11, andMR11 are the same as each other, and the arrangement patterns of thepixel values in the map data MG12, MB12, and MR12 are the same as eachother. Further, in the interpolation processing, the interpolatingmethods for generating six map data are the same as each other. Thismakes it possible to reduce a possibility that false colors occur, whichmakes it possible to enhance image quality of the captured image.

In the present embodiment, it is possible to control whether or not toperform the image segmentation processing, the interpolation processing,and the synthesis processing, which makes it possible to enhance imagequality of the captured image.

Modification Example 1-1

In the embodiment described above, for example, the synthesis processingsection 24 sums the pixel values at positions corresponding to eachother in two map data MG21 and MG22 to generate a pixel value at aposition corresponding to the positions in the map data MG3; however,this is not limitative. Alternatively, for example, as illustrated in animaging device 1A in FIG. 11 , pixel values at positions correspondingto each other in two map data MG21 and MG22 may be summed and asummation of the pixel values may be halved, thereby generating a pixelvalue at a position corresponding to the positions in the map data MG3.This makes it possible to make the pixel value in the map data MG3substantially equal to the pixel value in the image map data MPG. Thesame applies to the map data MB3 and MR3. This makes it possible toreduce the number of bits in a digital code indicating each of the pixelvalues in the map data MG3, MB3, and MR3 while maintaining thesignal-to-noise ratio. This consequently makes it possible to facilitatedesign of a dynamic range in the signal processing section 25.

Modification Example 1-2

In the embodiment described above, the arrangement patterns PAT of thepixel values in the image segmentation processing A1 are checkeredpatterns in units of one pixel value, but this is not limitative. Thepresent modification example is described in detail with some examplesbelow. It is to be noted that map data related to green (G) is describedbelow as an example, but the same applies to map data related to blue(B) and map data related to red (R).

(Other Checkered Patterns)

FIGS. 12A and 12B illustrate examples of map data MG11 and MG12 in acase where the arrangement patterns PAT of the pixel values arecheckered patterns in units of four pixel values arranged in two rowsand two columns. A pitch in the horizontal direction (the lateraldirection) in the arrangement patterns PAT illustrated in FIGS. 12A and12B is twice a pitch in the vertical direction in the arrangementpatterns PAT illustrated in FIGS. 8A and 8B. Similarly, a pitch in thevertical direction (the longitudinal direction) in the arrangementpatterns PAT illustrated in FIGS. 12A and 12B is twice a pitch in thevertical direction in the arrangement patterns PAT illustrated in FIGS.8A and 8B. The arrangement patterns PAT of the pixel values in the mapdata MG11 and MG12 are shifted by two pixels in the horizontal directionand the vertical direction from each other.

FIGS. 13A and 13B illustrate examples of the map data MG21 and MG22generated by performing the interpolation processing A2 on the basis ofthe map data MG11 and MG12 illustrated in FIGS. 12A and 12B. Forexample, it is possible for the interpolation processing section 23 todetermine a pixel value at a position where no pixel value is present byperforming the interpolation processing A2 on the basis of pixel valueslocated two rows above, two columns to the left of, two rows below, andtwo columns to the right of the position where no pixel value ispresent.

(Striped Pattern)

FIGS. 14A and 14B illustrate examples of the map data MG11 and MG12 in acase where the arrangement patterns PAT of the pixel values are stripedpatterns in which the positions where a pixel value is present and thepositions where no pixel value is present are arranged alternately inthe horizontal direction (the lateral direction). The arrangementpatterns PAT of the pixel values in the map data MG11 and MG12 areshifted by one pixel in the horizontal direction to each other.

FIGS. 15A and 15B illustrate examples of the map data MG21 and MG22generated by performing the interpolation processing A2 on the basis ofthe map data MG11 and MG12 illustrated in FIGS. 14A and 14B. It ispossible for the interpolation processing section 23 to determine apixel value at a position where no pixel value is present by performingthe interpolation processing A2 on the basis of pixel values located onecolumn to the left of and one column to the right of the position whereno pixel value is present.

FIGS. 16A and 16B illustrate examples of the map data MG11 and MG12 in acase where the arrangement patterns PAT of the pixel values are stripedpatterns in which the positions where a pixel value is present and thepositions where no pixel value is present are arranged alternately inthe vertical direction. The arrangement patterns PAT of the pixel valuesin the map data MG11 and MG12 are shifted by one pixel in the verticaldirection to each other.

FIGS. 17A and 17B illustrate examples of the map data MG21 and MG22generated by performing the interpolation processing A2 on the basis ofthe map data MG11 and MG12 illustrated in FIGS. 16A and 16B. It ispossible for the interpolation processing section 23 to determine apixel value at a position where no pixel value is present by performingthe interpolation processing A2 on the basis of pixel values located onerow above and one row below the position where no pixel value ispresent.

(Other Patterns)

In the examples described above, for example, the image segmentationprocessing section 22 generates two map data MG11 and MG12 by performingthe image segmentation processing A1 on the basis of one image map dataMPG, the interpolation processing section 23 generates two map data MG21and MG22 by performing the interpolation processing A2 on the two mapdata MG11 and MG12, and the synthesis processing section 24 generatesthe map data MG3 by performing the synthesis processing A3 on the basisof the two map data MG21 and MG22, but this is not limitative.Alternatively, for example, the image segmentation processing section 22may generate three map data MG11, MG12, and MG13 by performing the imagesegmentation processing A1 on the basis of, for example, one image mapdata MPG, the interpolation processing section 23 may generate three mapdata MG21, MG22, and MG23 by performing the interpolation processing A2on the three map data MG11, MG12, and MG13, and the synthesis processingsection 24 may generate map data MG3 by performing the synthesisprocessing A3 on the basis of the three map data MG21, MG22, and MG23.

FIGS. 18A, 18B, and 18C illustrate examples of the map data MG11, MG12,and MG13 in a case where the arrangement patterns PAT of the pixelvalues are patterns such as a so-called Bayer array. Specifically, forexample, in the map data MG11, as illustrated in FIG. 8A, in the regionX, a pixel value is present on the upper left and no pixel value ispresent on the lower left, the upper right and the lower right. In themap data MG12, as illustrated in FIG. 8B, in the region X, pixel valuesare present on the lower left and the upper right, and no pixel value ispresent on the upper left and the lower right. In the map data MG13, asillustrated in FIG. 8C, in the region X, a pixel value is present on thelower right, and no pixel value is present on the upper left, the lowerleft, and the upper right. The pixel value at each position in the mapdata MG 11 is the same as the pixel value at a corresponding position inthe image map data MPG, the pixel value at each position in the map dataMG12 is the same as the pixel value at a corresponding position in theimage map data MPG, and the pixel value at each position in the map dataMG13 is the same as the pixel value at a corresponding position in theimage map data MPG.

FIGS. 19A, 19B, and 19C illustrate examples of the map data MG21, MG22,and MG23 generated by performing the interpolation processing A2 on thebasis of the map data MG11, MG12, and MG13 illustrated in FIGS. 18A,18B, and 18C. In a case where pixel values are located above and belowthe position where no pixel value is present, the interpolationprocessing section 23 performs the interpolation processing A2 on themap data MG11 and MG13 (FIGS. 18A and 18C) on the basis of these twopixel values. In a case where pixel values are located to the left andthe right of the position where no pixel value is present, theinterpolation processing section 23 performs the interpolationprocessing A2 on the map data MG11 and MG13 on the basis of these twopixel values. In a case where pixel values are located on the upperleft, the lower left, the upper right, and the lower right of theposition where no pixel value is present, the interpolation processingsection 23 performs the interpolation processing A2 on the map data MG11and MG13 on the basis of these four pixel values. Thus, the map dataMG21 and MG23 are generated. In addition, the interpolation processingsection 23 generates map data MG22 by performing the interpolationprocessing A2 on the map data MG12 (FIG. 18B) on the basis of four pixelvalues located above, below and to the left, and to the right of theposition where no pixel value is present.

FIG. 20 illustrates an example of the map data MG3 generated byperforming the synthesis processing A3 on the basis of the map dataMG21, MG22, and MG23 illustrated in FIGS. 19A, 19B, and 19C. Thesynthesis processing section 24 sums pixel values at positionscorresponding to each other in the three map data MG21, MG22, and MG23to generate a pixel value at a position corresponding to the positionsin the map data MG3. In FIG. 20 , “3G” indicates that a pixel valuebecomes about three times the pixel value in the image map data MPG bythe synthesis processing A3.

Modification Example 1-3

In the embodiment described above, the interpolation processing section23 performs the interpolation processing A2, but the interpolatingmethod in the interpolation processing A2 may be changeable. The presentmodification example is described in detail below.

FIG. 21 illustrates a configuration example of an imaging device 2according to the present modification example. The imaging device 2includes an image processing section 30. The image processing section 30includes an interpolation controller 36 and an interpolation processingsection 33.

The interpolation controller 36 performs interpolation controlprocessing B1 on the basis of the image map data MPG, MPB, and MPRincluded in the image signal DT to determine the interpolation method inthe interpolation processing A2 in the interpolation processing section33. The interpolation controller 36 corresponds to a specific example ofan “interpolation controller” in the present disclosure.

The interpolation processing section 33 respectively performs theinterpolation processing A2 on the six map data MG11, MG12, MB11, MB12,MR11, and MR12 supplied from the image segmentation processing section22 with use of an interpolation method instructed by the interpolationcontroller 36 to generate six map data MG21, MG22, MB21, MB22, MR21, andMR22.

FIG. 22 schematically illustrates examples of the image segmentationprocessing A1, the interpolation control processing B1, theinterpolation processing A2, and the synthesis processing A3 in theimage processing section 30.

The interpolation controller 36 first performs synthesis processing B2on the basis of the image map data MPG, MPB, and MPR included in theimage signal DT to generate map data MW. In this synthesis processingB2, the interpolation controller 36 sums pixel values at positionscorresponding to each other in the three image map data MPR, MPB, andMPR, which makes it possible to generate a pixel value at a positioncorresponding to the positions in the map data MW.

Next, the interpolation controller 36 performs spatial frequencydetection processing B3 on the basis of this map data MW to detect aspatial frequency. In this spatial frequency detection processing B3,the interpolation controller 36 divides one frame image into a pluralityof image regions, and determines a spatial frequency in each of theimage regions on the basis of the map data MW.

Next, the interpolation controller 36 performs interpolation methoddetermination processing B4 on the basis of the spatial frequencydetermined by the spatial frequency detection processing B3 to determinethe interpolation method in the interpolation processing A2.

FIGS. 23A, 23B, and 23C illustrate examples of the interpolation methodin the interpolation processing A2. These diagrams illustrate the mapdata MG21 generated by performing the interpolation processing A2. Inthe interpolation method illustrated in FIG. 23A, a pixel value at aposition where no pixel value is present is determined on the basis ofpixel values located one row above and one row below the position whereno pixel value is present. That is, in the example in FIG. 23A, adirection (an interpolation direction) in which the interpolationprocessing is performed is the vertical direction (the longitudinaldirection). In the interpolating method illustrated in FIG. 23B, a pixelvalue at a position where no pixel value is present is determined on thebasis of pixel values located one column to the left of and one columnto the right of the position where no pixel value is present. That is,in the example in FIG. 23B, the direction (the interpolation direction)in which the interpolation processing is performed is the horizontaldirection (the lateral direction). In addition, in the interpolationmethod illustrated in FIG. 23C, a pixel value at a position where nopixel value is present is determined on the basis of pixel valueslocated one row above, one row below, one column to the left and onecolumn to the right of the position where no pixel value is present.That is, in the example in FIG. 23C, the direction (the interpolationdirection) in which the interpolation processing is performed is thevertical direction and the horizontal direction. It is to be noted thatthree examples have been described in FIGS. 23A to 23C, but theinterpolation method is not limited thereto.

In the interpolation method determination processing B4, theinterpolation controller 36 determines the interpolation method in theinterpolation processing A2 on the basis of the spatial frequency ineach of the image regions. Specifically, in a case where theinterpolation controller 36 decides that an image in a certain imageregion is a vertically striped pattern on the basis of the spatialfrequency in the certain image region, the interpolation controller 36selects the interpolation method (FIG. 23A) in which the interpolationdirection is the vertical direction. Further, for example, in a casewhere the interpolation controller 36 decides that an image in a certainimage region is a horizontally striped pattern on the basis of thespatial frequency in the certain image region, the interpolationcontroller 36 selects the interpolation method (FIG. 23B) in which theinterpolation direction is the horizontal direction. Then, theinterpolation controller 36 provides an instruction on the interpolationmethod for each of the image regions to the interpolation processingsection 33.

The interpolation processing section 33 respectively performs theinterpolation processing A2 on six map data MG11, MG12, MB11, MB12,MR11, and MR12 supplied from the image segmentation processing section22 with use of the interpolation method instructed for each of the imageregions from the interpolation controller 36 to generate six map dataMG21, MG22, MB21, MB22, MR21, and MR22. The interpolation methods forgenerating the six map data MG21, MG22, MB21, MB22, MR21, and MR22 arethe same as each other.

As described above, in the imaging device 2, the interpolation method inthe interpolation processing A2 is changeable, which makes it possibleto use an optimal interpolation method according to imaging pixels. Thismakes it possible to enhance image quality of a captured image.

In particular, in the imaging device 2, the map data MW is generated byperforming the synthesis processing B2 on the basis of the image mapdata MPG, MPB, and MPR, and the spatial frequency is detected on thebasis of this map data MW. Thus, it is possible to detect the spatialfrequency with high accuracy in the imaging device 2, and theinterpolation processing A2 is performed on the basis of thethus-obtained spatial frequency, which makes it possible to enhanceaccuracy of the interpolation processing A2. This consequently makes itpossible for the imaging device 2 to achieve a higher restoring effect,which makes it possible to enhance image quality of a captured image.

It is to be noted that in this example, the map data MW is generated byperforming the synthesis processing B2 on the basis of the image mapdata MPG, MPB, and MPR, and the spatial frequency is detected on thebasis of the map data MW, but this is not limitative. Alternatively, forexample, as in an image processing section 30A illustrated in FIG. 24 ,the spatial frequency detection processing B3 may be performed on thebasis of the image map data MPG related to green (G), and theinterpolation method in the interpolation processing A2 for generatingthe map data MG21 and MG22 of green (G) may be determined for each ofthe image regions on the basis of the spatial frequency in each of theimage regions obtained by this spatial frequency detection processingB3. The same applies to the image map data MPB and MPR. In addition,this is not limitative. For example, the spatial frequency detectionprocessing B3 may be performed on the basis of the image map data MPGrelated to green (G), the spatial frequency detection processing B3 maybe performed on the basis of the image map data MPB related to blue (B),the spatial frequency detection processing B3 may be performed on thebasis of the image map data MPR related to red (R), and theinterpolating methods in the interpolation processing A2 for generatingthe six map data MG21, MG22, MB21, MB22, MR21, and MR22 may bedetermined collectively for each of the image regions on the basis ofthe spatial frequency in each of the image regions obtained by thespatial frequency detection processing B3. In this case, it is possibleto cause the interpolation methods for generating the six map data MG21,MG22, MB21, MB22, MR21, and MR22 to be the same as each other.

Modification Example 1-4

In the embodiment described above, the image processing section 20performs the image segmentation processing A1, the interpolationprocessing A2, and the synthesis processing A3 on the basis of the imagemap data MPR related to red (R), the image map data MPG related to green(G), and the image map data MPB related to blue (B), but this is notlimitative. Alternatively, for example, the image segmentationprocessing A1, the interpolation processing A2, and the synthesisprocessing A3 may be performed on the basis of a luminance signal. Thepresent modification example is described in detail below.

FIG. 25 illustrates a configuration example of an imaging device 1Daccording to the present modification example. This imaging device 1Dincludes an image processing section 20D. The image processing section20D includes a Y/C separation section 29D, an image segmentationprocessing section 22D, an interpolation processing section 23D, asynthesis processing section 24D, and a signal processing section 25D.

The Y/C separation section 29D separates an RGB signal included in theimage signal DT into a luminance (Y) signal and a color (C) signal byperforming Y/C separation processing C1, and outputs the luminancesignal and the color signal as an image signal DT11. The image signalDT11 includes map data MY, MCr, and MCb. The map data MY includes pixelvalues for one frame image related to luminance (Y), the map data MCrincludes pixel values for one frame image related to an R-Y colordifference (Cr), and the map data MCb includes pixel values for oneframe image related to a B-Y color difference (Cb). Each of the pixelvalues is represented by a digital code having a plurality of bits. TheY/C separation section 29D corresponds to a specific example of a“generator” in the present disclosure, respectively.

The image segmentation processing section 22D performs the imagesegmentation processing A1 on the basis of the map data MY included inthe image signal DT11 supplied from the Y/C separation section 29D viathe switching section 21 to generate two map data MY11 and MY12. Inaddition, the image segmentation processing section 22D outputs the mapdata MCr and MCb included in the image signal DT11 as they are.

The interpolation processing section 23D respectively performs theinterpolation processing A2 on the two map data MY11 and MY12 suppliedfrom the image segmentation processing section 22D to generate two mapdata MY21 and MY22. In addition, the interpolation processing section23D outputs the map data MCr and MCb supplied from the imagesegmentation processing section 22D as they are.

The synthesis processing section 24D performs the synthesis processingA3 on the basis of the two map data MY21 and MY22 supplied from theinterpolation processing section 23D to generate one map data MY3. Then,the synthesis processing section 24D supplies the map data MY3 generatedby the synthesis processing A3 and the map data MCr and MCb suppliedfrom the interpolation processing section 23D as an image signal DT12 tothe signal processing section 25D.

The signal processing section 25D performs the predetermined signalprocessing on the basis of the image signal DT12 supplied from thesynthesis processing section 24D or the image signal DT11 supplied fromthe Y/C separation section 29D via the switching section 21. Then, thesignal processing section 25D outputs a processing result of thepredetermined signal processing as an image signal DT13.

FIG. 26 schematically illustrates examples of the image segmentationprocessing A1, the interpolation processing A2, and the synthesisprocessing A3 in the image processing section 20D.

The Y/C separation section 29D performs the Y/C separation processing C1to separate the RGB signal included in the image signal DT into theluminance (Y) signal and the color (C) signal. Specifically, the Y/Cseparation section 29D generates the map data MY, MCb, and MCr on thebasis of the image map data MPG, MPB, and MPR. The Y/C separationsection 29D generates a pixel value related to luminance (Y) with useof, for example, the following expression, on the basis of pixel valuesat positions corresponding to each other in the three image map dataMPG, MPB, and MPR.VY=VG×0.59+VB×0.11+VR×0.3In this expression, “VY” is a pixel value related to luminance (Y), “VG”is a pixel value related to green (G), “VB” is a pixel value related toblue (B), and “VR” is a pixel value related to red (R).

The image segmentation processing section 22D performs the imagesegmentation processing A1 on the basis of the thus-generated map dataMY to generate two map data MY11 and MY12. The interpolation processingsection 23D respectively performs the interpolation processing A2 on thetwo map data MY11 and MY12 to generate two map data MY21 and MY22. Thesynthesis processing section 24D performs the synthesis processing A3 onthe basis of the two map data MY21 and MY22 to generate one map dataMY3. Specifically, the synthesis processing section 24D sums pixelvalues at positions corresponding to each other in the two map data MY21and MY22 and halves a summation of the pixel values to generate a pixelvalue at a position corresponding to the positions in the map data MY3.The map data MY corresponds to a specific example of “first image mapdata” in the present disclosure. The map data MY11 and MY12 correspondto a specific example of a “plurality of first map data” in the presentdisclosure. The map data MY21 and MY22 correspond to a specific exampleof a “plurality of second map data” in the present disclosure. The mapdata MY3 corresponds to a specific example of “third map data” in thepresent disclosure.

As described above, in the imaging device 1D, it is possible to increasea signal-to-noise ratio (S/N ratio) for the luminance signal, whichmakes it possible to enhance image quality of a captured image. Inaddition, in this example, the image segmentation processing A1, theinterpolation processing A2, and the synthesis processing A3 areperformed only on the map data MY related to luminance (Y), which makesit possible to reduce a processing amount. This consequently makes itpossible to reduce power consumption in the imaging device 1D, forexample.

Anther Modification Example

In addition, two or more of these modification examples may be combined.

2. Second Embodiment

Next, description is given of an imaging device 3 according to a secondembodiment. The present embodiment differs from the first embodiment inconfigurations of blue and red photoelectric converters in an imagingsection. It is to be noted that components substantially the same asthose of the imaging device 1 according to the first embodimentdescribed above are denoted by the same reference numerals, anddescription thereof is omitted as appropriate.

FIG. 27 illustrates a configuration example of the imaging device 3according to the present embodiment. The imaging device 3 includes animaging section 40 and an image processing section 50.

FIG. 28 schematically illustrates cross-sectional configurations of theimaging pixels P in the imaging section 40. The semiconductor substrate100 includes one photodiode PD formed in the pixel region correspondingto one imaging pixel P. This photodiode PD is configured to receivelight of various wavelengths, unlike the photodiodes PDB and PDRaccording to the first embodiment described above. The insulating film101 is formed on the surface, on side of the imaging surface S, of thesemiconductor substrate 100, and a color filter 111 is formed on theinsulating film 101. Specifically, a color filter 111B or a color filter111R is selectively formed on the insulating film 101. The color filter111B allows blue (B) light to pass therethrough, and blocks red (R)light and green (G) light. The color filter 111R allows red (R) light topass therethrough, and blocks blue (B) light and green (G) light. Thecolor filter 111B and the photodiode PD are included in a photoelectricconverter that is configured to receive blue (B) light, and the colorfilter 111R and the photodiode PD are included in a photoelectricconverter that is configured to receive red (R) light. An insulatingfilm 112 is formed on the color filter 111. The insulating film 112 isconfigured using, for example, silicon dioxide (SiO₂). Then, atransparent electrode 102, a photoelectric conversion film 103G, atransparent electrode 104, and an on-chip lens 105 are formed in thisorder on the insulating film 112.

FIG. 29 schematically illustrates positions of photoelectric convertersin the region X in which four imaging pixels P are arranged. Thus, inthe imaging section 40, a photoelectric converter related to green (G)and a photoelectric converter related to blue (B) or red (R) arerespectively disposed in an upper layer and a lower layer in the pixelregion corresponding to one imaging pixel P. The photoelectricconverters related to blue (B) and red (R) are arranged in a checkeredpattern. That is, in the imaging section 40, the color filter 111B andthe color filter 111R are arranged in a checkered pattern. This makes itpossible for each of the imaging pixels P to generate a pixel signalrelated to green and a pixel signal related to blue or red in theimaging section 40.

With such a configuration, the imaging section 40 generates an imagesignal DT21 and the gain signal SGAIN. The image signal DT21 includestwo image map data MPG and MPBR. The image map data MPG includes pixelvalues for one frame image related to green (G), and the image map dataMPBR includes pixel values for one frame image related to blue (B) andred (R). In the image map data MPBR, pixel values related to blue (B)and pixel values related to red (R) are arranged in a checkered patterncorresponding to arrangement of color filters 111B and 111R.

The image processing section 50 (FIG. 27 ) includes an imagesegmentation processing section 52, an interpolation controller 56, aninterpolation processing section 53, a synthesis processing section 54,and a signal processing section 55.

The image segmentation processing section 52 performs the imagesegmentation processing A1 on the basis of the image map data MPG andMPBR included in the image signal DT21 supplied from the imaging section40 via the switching section 21 to generate four map data MG11, MG12,MR11, and MB12.

The interpolation controller 56 performs the interpolation controlprocessing B1 on the basis of the image map data MPG included in theimage signal DT21 to determine the interpolation method in theinterpolate interpolation processing A2 in the interpolation processingsection 53.

The interpolation processing section 53 respectively performs theinterpolation processing A2 on the four map data MG11, MG12, MR11, andMB12 supplied from the image segmentation processing section 52 with useof the interpolation method instructed from the interpolation controller56 to generate four map data MG21, MG22, MR21, and MB22.

The synthesis processing section 54 performs the synthesis processing A3on the basis of two map data MG21 and MG22 supplied from theinterpolation processing section 53 to generate one map data MG3. Then,the synthesis processing section 54 supplies the map data MG3 generatedby the synthesis processing A3 and the map data MR21 and MB22 suppliedfrom the interpolation processing section 53 as an image signal DT22 tothe signal processing section 55.

The signal processing section 55 performs the predetermined signalprocessing on the basis of the image signal DT22 supplied from thesynthesis processing section 54 or the image signal DT21 supplied fromthe imaging section 40 via the switching section 21. Then, the signalprocessing section 55 outputs a processing result of these predeterminedsignal processing as an image signal DT23.

FIG. 30 schematically illustrates examples of the image segmentationprocessing A1, the interpolation control processing B1, theinterpolation processing A2, and the synthesis processing A3 in theimage processing section 50.

The image segmentation processing section 52 performs the imagesegmentation processing A1 on the basis of the image map data MPG togenerate two map data MG11 and MG12. In addition, the image segmentationprocessing section 52 performs the image segmentation processing A1 onthe basis of the image map data MPBR to generate two map data MR11 andMB12. In the map data MR11, as illustrated in FIG. 30 , pixel valuesrelated to red (R) are present on the upper left and the lower right inthe region X, and no pixel value is present on the lower left and theupper right in the region X. In addition, in the map data MB12, pixelvalues related to blue (B) are present on the lower left and the upperright in the region X, and no pixel value is present on the upper leftand lower right in the region X. That is, in this example, thearrangement patterns PAT of pixel values in the image segmentationprocessing A1 are checkered pattern to correspond to checkered patternarrangement of the color filter 111B and 111R in the imaging section 40.Accordingly, pixel values related to red (R) included in the image mapdata MPBR are included only in the map data MR11, and pixel valuesrelated to blue (B) included in the image map data MPBR are includedonly in the map data MB12. As illustrated in FIG. 30 , the map data MG11and MR11 have the same arrangement pattern PAT, and the map data MG12and MB12 have the same arrangement pattern PAT.

The interpolation controller 56 performs the spatial frequency detectionprocessing B3 on the basis of the image map data MPG related to green(G) to detect a spatial frequency. Then, the interpolation controller 56performs the interpolation method determination processing B4 todetermine the interpolation method in the interpolation processing A2for each of the image regions on the basis of the spatial frequencydetermined by the spatial frequency detection processing B3. Then, theinterpolation controller 56 provides an instruction on the interpolationmethod for each of the image regions to the interpolation processingsection 53.

The interpolation processing section 53 respectively performs theinterpolation processing A2 on the four map data MG11, MG12, MR11, andMB12 supplied from the image segmentation processing section 52 with useof the interpolating method instructed for each of the image regionsfrom the interpolation controller 56 to generate four map data MG21,MG22, MR21, and MB22. The interpolating methods for generating the fourmap data MG21, MG22, MR21, and MB22 are the same as each other.

The synthesis processing section 54 performs the synthesis processing A3on the basis of two map data MG21 and MG22 to generate one map data MG3.Specifically, the synthesis processing section 54 sums pixel values atpositions corresponding to each other in the two map data MG21 and G22and halves a summation of the pixel values to generate a pixel value ata position corresponding to the positions in the map data MG3.

The image map data MPG and MPBR respectively correspond to specificexamples of “first image map data” and “second image map data” in thepresent disclosure, The map data MR11 and MB12 correspond to a specificexample of a “plurality of fourth map data” in the present disclosure.The map data MR21 and MB22 correspond to a specific example of a“plurality of fifth map data” in the present disclosure.

As described above, in the imaging device 3, for example, the imagesegmentation processing A1 is performed on the basis of the image mapdata MPG to generate the map data MG11 and MG12, the interpolationprocessing A2 is respectively performed on the map data MG11 and MG12 togenerate the map data MG21 and MG22, and the synthesis processing A3 isperformed on the basis of the map data MG21 and MG22 to generate the mapdata MG3. This makes it possible to increase a signal-to-noise ratio(S/N ratio) in the map data MG3 and enhance image quality of a capturedimage in the imaging device 3, as in the first embodiment describedabove.

In addition, in the imaging device 3, the arrangement patterns PAT ofpixel values in the image segmentation processing A1 are checkeredpatterns to correspond to the checkered pattern arrangement of the colorfilters 111B and 111R in the imaging section 40. The arrangementpatterns PAT of pixel values in the map data MG11 and MR11 are the sameas each other, and the arrangement patterns PAT of pixel values in themap data MG12 and MB12 are the same as each other. This makes itpossible for the image segmentation processing section 52 to perform theimage segmentation processing A1 by the same method on the basis of twoimage map data MPG and MPBR, which makes it possible to simplify acircuit configuration of the image segmentation processing section 52.Further, as in a case of the imaging device 1, it is possible to reducea possibility that false colors occur and enhance image quality of acaptured image.

In addition, in the imaging device 3, the interpolating methods forgenerating the map data MG21, MG22, MR21, and MB22 in the interpolationprocessing A2 are the same as each other. This makes it possible for theinterpolation processing section 53 to generate four map data MG21,MG22, MR21, and MB22 with use of the same interpolating method, whichmakes it possible to simplify a circuit configuration of theinterpolation processing section 53. In addition, as in the case of theimaging device 1, it is possible to reduce a possibility that falsecolors occur, and enhance image quality of a captured image.

In addition, in the imaging device 3, the spatial frequency is detectedon the basis of the image map data MPG all over which pixel valuesrelated to green (G) are located, and the interpolation method in theinterpolation processing A2 is determined on the basis of the detectedspatial frequency. This makes it possible for the imaging device 3 todetect the spatial frequency with high accuracy, which makes it possibleto enhance accuracy of the interpolation processing A2. Thisconsequently makes it possible for the imaging device 3 to achieve ahigher restoring effect, which makes it possible to enhance imagequality of a captured image.

In addition, in the imaging device 3, as in the imaging device 1according to the first embodiment described above, it is possible tocontrol whether or not to perform the image segmentation processing, theinterpolation processing, and the synthesis processing. Accordingly, inthe imaging device 3, for example, in a case where an image of a darksubject is captured, performing the image segmentation processing A1,the interpolation processing A2, and the synthesis processing A3 makesit possible to increase a signal-to-noise ratio (S/N ratio) in thecaptured image. For example, in a case where an image of a brightsubject is captured, the image segmentation processing A1, theinterpolation processing A2, and the synthesis processing A3 are notperformed, which makes it possible to increase resolution in thecaptured image. This consequently makes it possible for the imagingdevice 3 to enhance image quality of the captured image.

As described above, in the present embodiment, the image segmentationprocessing, the interpolation processing, and the synthesis processingare performed, which makes it possible to increase the signal-to-noiseratio in the captured image. This makes it possible to enhance imagequality of the captured image.

In the present embodiment, the arrangement patterns of pixel values inthe image segmentation processing are checkered patterns to correspondto checkered pattern arrangement of the color filters in the imagingsection, which makes it possible to simplify a circuit configuration ofthe image segmentation processing section, and to reduce a possibilitythat false colors occurs and enhance image quality of the capturedimage.

In the present embodiment, in the interpolation processing, theinterpolation methods for generating four map data are the same as eachother, which makes it possible to simplify a circuit configuration ofthe interpolation processing section, and to reduce a possibility thatfalse colors occur and enhance image quality of the captured image.

In the present embodiment, the spatial frequency is detected on thebasis of the mage map data MPG over which the pixel values related togreen (G) are located to determine the interpolation method in theinterpolation processing, which makes it possible to detect the spatialfrequency with high accuracy and enhance image quality of the capturedimage.

In the present embodiment, it is possible to control whether or not toperform the image segmentation processing, the interpolation processing,and the synthesis processing, which makes it possible to enhance imagequality of the captured image.

3. Third Embodiment

Next, description is given of an imaging device 4 according to a thirdembodiment. The present embodiment differs from the first embodimentdescribed above in an arrangement density of photoelectric convertersthat are configured to receive blue (B) light and red (R) light in animaging section. It is to be noted that components substantially thesame as those of the imaging device 1 according to the first embodimentdescribed above are denoted by the same reference numerals, anddescription thereof is omitted as appropriate.

FIG. 31 illustrates a configuration example of the imaging device 4according to the present embodiment. The imaging device 4 includes animaging section 60 and an image processing section 70.

FIG. 32 schematically illustrates cross-sectional configurations of theimaging pixels P in the imaging section 60. FIG. 33 schematicallyillustrates positions of photoelectric converters in the region X inwhich four imaging pixels P are arranged. The semiconductor substrate100 includes photodiodes PDR2 and PDB2 formed in the region Xcorresponding to the four imaging pixels P. The photodiode PDR2 is aphotoelectric converter that is configured to receive red (R) light asin the photodiode PDR, and the photodiode PDB2 is a photoelectricconverter that is configured to receive blue (B) light as in thephotodiode PDB. The photodiode PDR2 and the photodiode PDB2 are formedand stacked in the semiconductor substrate 100 in the region Xcorresponding to the four imaging pixels P in such a manner that thephotodiode PDB2 is located on side of the imaging surface S. That is, inthe imaging section 10 according to the first embodiment, thephotodiodes PDB and PDR are formed and stacked in the pixel regioncorresponding to one imaging pixel P, whereas in the imaging section 60according to the present embodiment, the photodiodes PDB2 and PDR2 areformed and stacked in the region X corresponding to the four imagingpixels P. Accordingly, in the imaging section 60, four photoelectricconverters related to green (G), one photoelectric converter related toblue (B), one photoelectric converter related to red (R) is formed andstacked in the region X corresponding to the four imaging pixels P. Inother words, in the imaging section 60, an arrangement density of thephotoelectric converters related to blue (B) is ¼ of an arrangementdensity of the photoelectric converters related to green (G), and anarrangement density of the photoelectric converters related to red (R)is ¼ of the arrangement density of the photoelectric converters relatedto green (G). The insulating film 112 is formed on the color filter 111.The insulating film 112 is configured using, for example, silicondioxide (SiO₂). The insulating film 101 is formed on the semiconductorsubstrate 100, and the transparent electrode 102, the photoelectricconversion film 103G, the transparent electrode 104, and the on-chiplens 105 are formed in this order on the insulating film 101.

With such a configuration, the imaging section 60 generates an imagesignal DT31 and the gain signal SGAIN. The image signal DT31 includesthree image map data MPG, MPB, and MPR. The image map data MPG includespixel values for one frame image related to green (G). The image mapdata MPB includes pixel values for one frame image related to blue (B).The image map data MPR includes pixel values for one frame image relatedto red (R). The number of pixel values in the image map data MPB is ¼ ofthe number of pixel values in the image map data MPG, and the number ofpixel values in the image map data MPR is ¼ of the number of pixelvalues in the image map data MPG. Four pixel values in the image mapdata MPG are associated with one pixel value in the image map data MPB,and are also associated with one pixel value in image map data MPR.

The image processing section 70 (FIG. 31 ) includes an imagesegmentation processing section 72, an interpolation processing section73, a synthesis processing section 74, and a signal processing section75.

The image segmentation processing section 72 performs the imagesegmentation processing A1 on the basis of the image map data MPG, MPB,and MPR included in the image signal DT31 supplied from the imagingsection 60 via the switching section 21 to generate six map data MG11,MG12, MB11, MB12, MR11, and MB12.

The interpolation processing section 73 respectively performs theinterpolation processing A2 on the six map data MG11, MG12, MB11, MB12,MR11, and MR12 supplied from the image segmentation processing section72 to generate six map data MG21, MG22, MB21, MB22, MR21, and MB22.

The synthesis processing section 74 performs the synthesis processing A3on the basis of the six map data MG21, MG22, MB21, MB22, MR21, and MR22supplied from the interpolation processing section 73 to generates threemap data MG3, MB3, and MR3. Then, the synthesis processing section 74supplies the map data MG3, MB3, and MR3 generated by the synthesisprocessing A3 as an image signal DT32 to the signal processing section75.

The signal processing section 75 performs the predetermined signalprocessing on the basis of the image signal DT32 supplied from thesynthesis processing section 74 or the image signal DT31 supplied fromthe imaging section 60 via the switching section 21. Then, the signalprocessing section 75 outputs a processing result of these predeterminedsignal processing as an image signal DT33.

FIG. 34 schematically illustrates examples of the image segmentationprocessing A1, the interpolation processing A2, and the synthesisprocessing A3 in the image processing section 70.

The image segmentation processing section 72 performs the imagesegmentation processing A1 on the basis of the image map data MPG, MPB,and MPR to generate six map data MG11, MG12, MB11, MB12, MR11, and MR12.As illustrated in FIG. 34 , the arrangement patterns PAT of pixel valuesin the map data MG11 and MG12 are checkered patterns in units of fourpixel values (FIGS. 12A and 12B). In contrast, the arrangement patternsPAT of pixel values in the map data MB11, MB12, MR11, and MR12 arecheckered patterns (FIGS. 8A and 8B) in units of one pixel value. Thatis, the unit in the checkered pattern in each of the map data MG11 andMG12 is four times the unit in the checkered pattern in each of map dataMB11, MB12, MR11, and MR12 correspondingly to the arrangement densitiesof photoelectric converters related to green (G), blue (B), and red (R)in the imaging section 60.

The interpolation processing section 73 respectively performs theinterpolation processing A2 on the six map data MG11, MG12, MB11, MB12,MR11, and MR12 supplied from the image segmentation processing section22 to generate six map data MG21, MG22, MB21, MB22, MR21, and MR22. In acase where the interpolation processing A2 is performed on the map dataMG11 and MG12, it is possible for the interpolation processing section73 to use the interpolating methods illustrated in FIGS. 13A and 13B.The interpolating methods for generating the map data MB21, MB22, MR21,and MR22 are the same as each other. In addition, it is possible tocause the interpolation method for generating each of the map data MG21and MG22 to be the same as the interpolation method for generating eachof the map data MB21, MB22, MR21, and MR22. Specifically, for example,it is possible to cause interpolation directions in these twointerpolation methods to be the same as each other.

The synthesis processing section 74 performs the synthesis processing A3on the basis of the six map data MG21, MG22, MB21, MB22, MR21, and MR22to generate three map data MG3, MB3, and MR3.

As described above, in the imaging device 4, for example, the imagesegmentation processing A1 is performed on the basis of the image mapdata MPG to generate the map data MG11 and MG12, the interpolationprocessing A2 is respectively performed on the map data MG11 and MG12 togenerate the map data MG21 and MG22, and the synthesis processing A3 isperformed on the basis of the map data MG21 and MG22 to generate the mapdata MG3. The same applies to the image map data MPB and MPR. This makesit possible to increase a signal-to-noise ratio (SN ratio) in the mapdata MG3, MB3, and MR3 and enhance image quality of a captured image inthe imaging device 4, as in the first embodiment described above.

In addition, in the imaging device 4, the unit in the checkered patternin each of the map data MG11 and MG12 is four times the unit in thecheckered pattern in each of the map data MB11, MB12, MR11, and MR12correspondingly to the arrangement densities of the photoelectricconverters related to green (G), blue (B), and red (R) in the imagingsection 60. This makes it possible for the image segmentation processingsection 72 to perform the image segmentation processing A1 by a similarmethod on the basis of the three image map data MPG, MPB, and MPR, whichmakes it possible to simplify a circuit configuration of the imagesegmentation processing section 72. Further, as in the case of theimaging device 1, it is possible to reduce a possibility that falsecolors occur, and enhance image quality of a captured image.

In addition, in the imaging device 4, the interpolating methods forgenerating the map data MB21, MB22, MR21, and MR22 in the interpolationprocessing A2 are the same as each other. This makes it possible for theinterpolation processing section 73 to generate four map data MB21,MB22, MR21, and MR22 with use of the same interpolating method, whichmakes it possible to simplify a circuit configuration of theinterpolation processing section 73. In addition, in the interpolationprocessing A2, the interpolation method for generating each of the mapdata MG21 and MG22 is similar to the interpolation method for generatingeach of the map data MB21, MB22, MR21, and MR22, which makes it possibleto reduce a possibility that false colors occur, and enhance imagequality of a captured image as in the case of the imaging device 1.

In addition, in the imaging device 4, as in the imaging device 1according to the first embodiment described above, it is possible tocontrol whether or not to perform the image segmentation processing, theinterpolation processing, and the synthesis processing. Accordingly, inthe imaging device 4, for example, in a case where an image of a darksubject is captured, performing the image segmentation processing A1,the interpolation processing A2, and the synthesis processing A3 makesit possible to increase a signal-to-noise ratio (S/N ratio) in thecaptured image. For example, in a case where an image of a brightsubject is captured, the image segmentation processing A1, theinterpolation processing A2, and the synthesis processing A3 are notperformed, which makes it possible to increase resolution in thecaptured image. This consequently makes it possible for the imagingdevice 4 to enhance image quality of the captured image.

As described above, in the present embodiment, the image segmentationprocessing, the interpolation processing, and the synthesis processingare performed, which makes it possible to increase the signal-to-noiseratio in the captured image. This makes it possible to enhance imagequality of the captured image.

In the present embodiment, the unit in the checkered pattern in each ofthe map data MG11 and MG12 is four times the unit in the checkeredpattern in each of the map data MB11, MB12, MR11, and MR12correspondingly the arrangement densities of the photoelectricconverters related to green, blue, and red in the imaging section, whichmakes it possible to simplify a circuit configuration of the imagesegmentation processing section and to reduce a possibility that falsecolors occurs and enhance image quality of the captured image.

In the present embodiment, in the interpolation processing, theinterpolation methods for generating the map data MB21, MB22, MR21, andMR22 are the same as each other, which makes it possible to simplify acircuit configuration of the interpolation processing section, and toreduce a possibility that false colors occur.

In the present embodiment, in the interpolation processing, theinterpolation method for generating each of the map data MG21 and MG22is similar to the interpolation method for generating each of the mapdata MB21, MB22, MR21, and MR22, which makes it possible to reduce apossibility that false colors occur and enhance image quality of thecaptured image.

In the present embodiment, it is possible to control whether or not toperform the image segmentation processing, the interpolation processing,and the synthesis processing, which makes it possible to enhance imagequality of the captured image.

Modification Example 3-1

In the embodiment described above, the image processing section 70performs the image segmentation processing A1, the interpolationprocessing A2, and the synthesis processing A3 on the basis of the imagemap data MPR related to red (R), the image map data MPG related to green(G), and the image map data MPB related to blue (B), but this is notlimitative. Alternatively, the image segmentation processing A1, theinterpolation processing A2, and the synthesis processing A3 may beperformed on the basis of a luminance signal as in a case of the imagingdevice 1D according to the modification example of the first embodiment(FIG. 25 ). The present modification example is described in detailbelow.

FIG. 35 illustrates a configuration example of an imaging device 4Aaccording to the present modification example. The imaging device 4Aincludes an image processing section 70A. The image processing section70A includes a Y/C separation section 79A, an image segmentationprocessing section 72A, an interpolation processing section 73A, asynthesis processing section 74A, and a signal processing section 75A.

The Y/C separation section 79A separates an RGB signal included in theimage signal DT31 into a luminance (Y) signal and a color (C) signal byperforming the Y/C separation processing C1, and outputs the luminancesignal and the color signal as an image signal DT41. The image signalDT41 includes map data MY, MCr, and MCb. The map data MY includes pixelvalues for one frame image related to luminance (Y), the map data MCrincludes pixel values for one frame image related to an R-Y colordifference (Cr), and the map data MCb includes pixel values for oneframe image related to a B-Y color difference (Cb). The number of pixelvalues in the map data MCr is ¼ of the number of pixel values in the mapdata MY. Similarly, the number of pixel values in the map data MCb is ¼of the number of pixel values in the map data MY.

The image segmentation processing section 72A performs the imagesegmentation processing A1 on the basis of the map data MY included inthe image signal DT41 supplied from the Y/C separation section 79A viathe switching section 21 to generate two map data MY11 and MY12. Inaddition, the image segmentation processing section 72A outputs the mapdata MCr and MCb included in the image signal DT41 as they are.

The interpolation processing section 73A respectively performs theinterpolation processing A2 on the two map data MY11 and MY12 suppliedfrom the image segmentation processing section 72A to generate two mapdata MY21 and MY22. In addition, the interpolation processing section73A outputs the map data MCr and MCb supplied from the imagesegmentation processing section 72A as they are.

The synthesis processing section 74A performs the synthesis processingA3 on the basis of the two map data MY21 and MY22 supplied from theinterpolation processing section 73A to generate one map data MY3. Then,the synthesis processing section 74A supplies the map data MY3 generatedby the synthesis processing A3 and the map data MCr and MCb suppliedfrom the interpolation processing section 73A as an image signal DT42 tothe signal processing section 75A.

The signal processing section 75A performs the predetermined signalprocessing on the basis of the image signal DT42 supplied from thesynthesis processing section 74A or the image signal DT41 supplied fromthe Y/C separation section 79A via the switching section 21. Then, thesignal processing section 75A outputs a processing result of thesepredetermined signal processing as an image signal DT43.

FIG. 36 schematically illustrates examples of the image segmentationprocessing A1, the interpolation processing A2, and the synthesisprocessing A3 in the image processing section 70A.

The Y/C separation section 79A performs the Y/C separation processing C1to separate the RGB signal included in the image signal DT31 into theluminance (Y) signal and the color (C) signal. Specifically, the Y/Cseparation section 79A generates the map data MY, MCb, and MCr on thebasis of the image map data MPG, MPB, and MPR. The Y/C separationsection 79A generates a pixel value related to luminance (Y) with useof, for example, the following expression, on the basis of pixel valuesat positions corresponding to each other in the three image map dataMPG, MPB, and MPR.VY1=VG1×0.59+VB/4×0.11+VR/4×0.3VY2=VG2×0.59+VB/4×0.11+VR/4×0.3VY3=VG3×0.59+VB/4×0.11+VR/4×0.3VY4=VG4×0.59+VB/4×0.11+VR/4×0.3In this expression, each of “VY1” to “VY4” is a pixel value related toluminance (Y), each of “VG1” to “VG4” is a pixel value related to green(G), “VB” is a pixel value related to blue (B), and “VR” is a pixelvalue related to red (R). Each of “VY1” and “VG1” indicates a pixelvalue on the upper left in the region X, each of “VY2” and “VG2”indicates a pixel value on the upper right in the region X, each of“VY3” and “VG3” indicate a pixel value on the lower left in the regionX, and each of “VY4” and “VG4” indicate a pixel value on the lower rightin the region X.

The image segmentation processing section 72A performs the imagesegmentation processing A1 on the basis of the thus-generated map dataMY to generate two map data MY11 and MY12. The interpolation processingsection 73A respectively performs the interpolation processing A2 on thetwo map data MY11 and MY12 to generate two map data MY21 and MY22. Thesynthesis processing section 74A performs the synthesis processing A3 onthe basis of the two map data MY21 and MY22 to generate one map dataMY3.

As described above, in the imaging device 4A, it is possible to increasea signal-to-noise ratio (S/N ratio) for the luminance signal, whichmakes it possible to enhance image quality of a captured image. Inaddition, in this example, the image segmentation processing A1, theinterpolation processing A2, and the synthesis processing A3 areperformed only on the map data MY related to luminance (Y), which makesit possible to reduce a processing amount. This consequently makes itpossible to reduce power consumption in the imaging device 4A, forexample.

Modification Example 3-2

Each of the modification examples of the first embodiment may be appliedto the imaging device 4 according to the embodiment described above.Specifically, for example, as in the imaging device 2 (FIG. 21 )according to the modification example of the first embodiment describedabove, the interpolation method in the interpolation processing A2 inthe interpolation processing section 73 may be controlled by performingthe interpolation control processing B1 on the basis of the image mapdata MPG, MPB, and MPR included in the image signal DT31.

4. Fourth Embodiment

Next, description is given of an imaging device 5 according to a fourthembodiment. In the present embodiment, an imaging section includes aphotoelectric converter that is configured to receive infrared (IR)light in addition to photoelectric converters that are configured toreceive green (G) light, blue (B) light, and red (R) light. It is to benoted that components substantially the same as those of the imagingdevice 1 according to the first embodiment described above are denotedby the same reference numerals, and description thereof is omitted asappropriate.

FIG. 37 illustrates a configuration example of the imaging device 5according to the present embodiment. The imaging device 5 includes animaging section 80 and an image processing section 90.

FIG. 38 schematically illustrates cross-sectional configurations of theimaging pixels P in the imaging section 80. FIG. 39 schematicallyillustrates positions of photoelectric converters in the region X inwhich four imaging pixels P are arranged. The semiconductor substrate100 includes the photodiode PD formed in the pixel region correspondingto one imaging pixel P. This photodiode PD is configured to receivelight of various wavelengths corresponding to visible light. Theinsulating film 101 is formed on the surface, on side of the imagingsurface S, of the semiconductor substrate 100, and the color filter 111is formed on the insulating film 101. Specifically, in this example, thecolor filter 111R of red (R), color filters 111G of green (G), and thecolor filter 111B of blue (B) are respectively formed on the upper left,the lower left and the upper right, and the lower right in the region Xcorresponding to four imaging pixels P on the insulating film 101. Thecolor filter 111R allows red (R) light to pass therethrough, and blocksblue (B) light and green (G) light. The color filter 111G allows green(G) light to pass therethrough, and blocks red (R) light and blue (B)light. The color filter 111B allows blue (B) light to pass therethrough,and blocks red (R) light and green (G) light. The color filter 111R andthe photodiode PD are included in a photoelectric converter that isconfigured to receive red (R) light. The color filter 111G and thephotodiode PD are included in a photoelectric converter that isconfigured to receive green (G) light. The color filter 111B and thephotodiode PD are included in a photoelectric converter that isconfigured to receive blue (B) light. The color filters 111R, 111G, and111B are arranged in a so-called Bayer array.

The insulating film 112 is formed on the color filter 111. Then, thetransparent electrode 102, a photoelectric conversion film 10318, andthe transparent electrode 104 are formed in this order on the insulatingfilm 112. The transparent electrodes 102 and 104 are electrodes thatallow red light, green light, blue light, and infrared light to passtherethrough. The photoelectric conversion film 1031R is a photoelectricconversion film that is configured to receive green (G) light, andallows red light, green light, and blue light to pass therethrough. Thephotoelectric conversion film 1031R and the transparent electrodes 102and 104 are included in a photoelectric converter that is configured toreceive infrared (IR) light. The on-chip lens 105 is formed on thetransparent electrode 104.

As described above, in the imaging section 80, the photoelectricconverter related to infrared (IR) and the photoelectric converterrelated to red (R), green (G), or blue (B) are respectively disposed inan upper layer and a lower layer in the pixel region corresponding toone imaging pixel P, as illustrated in FIG. 39 . The photoelectricconverters related to red (R), green (G), and blue (B) are arranged in aBayer array. This makes it possible for each of the imaging pixels P inthe imaging section 80 to generate a pixel signal related to infraredand a pixel signal related to red, green, or blue.

With such a configuration, the imaging section 80 generates an imagesignal DT51 and the gain signal SGAIN. The image signal DT51 includestwo image map data MPIR and MPRGB. The image map data MPIR includespixel values for one frame image related to infrared (IR), and the imagemap data MPRGB includes pixel values for one frame image related to red(R), green (G), and blue (B).

The image processing section 90 (FIG. 37 ) includes an imagesegmentation processing section 92, an interpolation processing section93, a synthesis processing section 94, and a signal processing section95.

The image segmentation processing section 92 performs the imagesegmentation processing A1 on the basis of the image map data MPIRincluded in the image signal DT51 supplied from the imaging section 80via the switching section 21 to generate three map data MIR12, MIR11,and MIR13, and performs the image segmentation processing A1 on thebasis of the image map data MPRGB included in the image signal DT51 togenerate three map data MG12, MR11, and MB13.

The interpolation processing section 93 respectively performs theinterpolation processing A2 on the six map data MIR12, MIR11, MIR13,MG12, MR11, and MB13 supplied from the image segmentation processingsection 92 to generate six map data MIR22, MIR21, MIR23, MG22, MR21, andMB23.

The synthesis processing section 94 performs the synthesis processing A3on the basis of three map data MIR22, MIR21, and MIR23 supplied from theinterpolation processing section 93 to generate map data MIR3. Then, thesynthesis processing section 94 supplies the map data MIR3 generated bythe synthesis processing A3 and the map data MG22, MR21, and MB23supplied from the interpolation processing section 93 as an image signalDT52 to the signal processing section 95.

The signal processing section 95 performs the predetermined signalprocessing on the basis of the image signal DT52 supplied from thesynthesis processing section 94 or the image signal DT51 supplied fromthe imaging section 60 via the switching section 21. Then, the signalprocessing section 95 outputs a processing result of these predeterminedsignal processing as an image signal DT53.

FIG. 40 schematically illustrates examples of the image segmentationprocessing A1, the interpolation processing A2, and the synthesisprocessing A3 in the image processing section 90.

The image segmentation processing section 92 performs the imagesegmentation processing A1 on the basis of the image map data MPIR togenerate three map data MIR12, MIR11, and MIR13, and performs the imagesegmentation processing A1 on the basis of the image map data MPRGB togenerate three map data MG12, MR11, and MB13. As illustrated in FIG. 40, arrangement patterns PAT of pixel values in the map data MIR12, MIR11,and MIR13 are patterns (FIGS. 18A to 18C) corresponding to the Bayerarray. The same applies to arrangement patterns PAT of pixel values inthe map data MG12, MR11, and MB13. That is, in this example, thearrangement patterns PAT of pixel values in the image segmentationprocessing A1 are patterns corresponding to the Bayer array indicatingarrangement of the color filters 111R, 111G, and 111B in the imagingsection 80. Accordingly, pixel value for the red (R) included in theimage map data MPRGB are included only in the map data MR11, pixelvalues for green (G) included in the image map data MPRGB are includedonly in the map data MG12, and pixel values for blue (B) included in theimage map data MPRGB are included only in the map data MB13. Asillustrated in FIG. 40 , the map data MIR12 and MG12 have the samearrangement pattern PAT, the map data MIR11 and MR11 have the samearrangement pattern PAT, and the map data MIR13 and MB13 have the samearrangement pattern PAT.

The interpolation processing section 93 respectively performs theinterpolation processing A2 on the six map data MIR12, MIR11, MIR13,MG12, MR11, and MB13 supplied from the image segmentation processingsection 92 to generate six map data MIR22, MIR21, MIR23, MG22, MR21, andMB23. In a case where the interpolation processing A2 is performed onthe map data MIR12, MIR11, and MIR13, it is possible for theinterpolation processing section 93 to use the interpolation methodsillustrated in FIGS. 19A to 19C, for example. The same applies to theinterpolation processing A2 on the map data MG12, MR11, and MB13. Theinterpolation methods for generating the respective map data MIR22 andMG22 are the same as each other. The interpolation methods forgenerating the respective map data MIR21 and MR21 are the same as eachother. The interpolation methods for generating the respective map dataMIR23 and MB23 are the same as each other.

The synthesis processing section 94 performs the synthesis processing A3on the basis of three map data MIR22, MIR21, and MIR23 to generate mapdata MIR3.

The image map data MPIR and MPRGB respectively correspond to specificexamples of “first image map data” and “second image map data” in thepresent disclosure. The map data MIR12, MIR11, and MIR13 correspond to aspecific example of a “plurality of first map data” in the presentdisclosure. The map data MIR22, MIR21, and MIR23 correspond to aspecific example of a “plurality of second map data” in the presentdisclosure. The map data MIR3 corresponds to a specific example of“third map data” in the present disclosure. The map data MG12, MR11, andMB13 correspond to a specific example of a “plurality of fourth mapdata” in the present disclosure. The map data MG22, MR21, and MB23correspond to a specific example of a “plurality of fifth map data” inthe present disclosure.

As described above, in the imaging device 5, for example, the imagesegmentation processing A1 is performed on the basis of the image mapdata MPIR to generate the map data MIR12, MIR11, and MIR13, theinterpolation processing A2 is respectively performed on the map dataMIR12, MIR11, and MIR13 to generate the map data MIR22, MIR21, andMIR23, and the synthesis processing A3 is performed on the basis of themap data MIR22, MIR21, and MIR23 to generate the map data MIR3. Thismakes it possible to increase a signal-to-noise ratio (S/N ratio) in themap data MIR3 and enhance image quality of a captured image in theimaging device 5, as in the first embodiment described above.

In addition, in the imaging device 5, the arrangement patterns PAT ofpixel values in the image segmentation processing A1 are patternscorresponding to the Bayer array to correspond to arrangement of colorfilters 111R, 111G, and 111B in the imaging section 80. The arrangementpatterns PAT of pixel values in the map data MIR12 and MG12 are the sameas each other. The arrangement patterns PAT of pixel values in the mapdata MIR11 and MR11 are the same as each other. The arrangement patternsPAT of pixel values in the map data MIR13 and MB13 are the same as eachother. This makes it possible for the image segmentation processingsection 92 to perform the mage segmentation processing A1 by the samemethod on the basis of two image map data MPIR and MPRGB, which makes itpossible to simplify a circuit configuration of the image segmentationprocessing section 92.

In addition, in the imaging device 5, in the interpolation processingA2, the interpolation methods for generating the map data MIR22 and MG22are the same as each other, the interpolation methods for generating themap data MIR21 and MR21 are the same as each other, and theinterpolation methods for generating the map data MIR23 and MB23 are thesame as each other, which makes it possible to simplify a circuitconfiguration of the interpolation processing section 93.

In addition, in the imaging device 5, as in imaging device 1 accordingto the first embodiment described above, it is possible to controlwhether or not to perform the image segmentation processing, theinterpolation processing, and the synthesis processing. Accordingly, inthe imaging device 3, for example, in a case where an image of a darksubject is captured, performing the image segmentation processing A1,the interpolation processing A2, and the synthesis processing A3 makesit possible to increase a signal-to-noise ratio (S/N ratio) in thecaptured image. For example, in a case where an image of a brightsubject is captured, the image segmentation processing A1, theinterpolation processing A2, and the synthesis processing A3 are notperformed, which makes it possible to increase resolution in thecaptured image. This consequently makes it possible for the imagingdevice 5 to enhance image quality of the captured image.

As described above, in the present embodiment, the image segmentationprocessing, the interpolation processing, and the synthesis processingare performed, which makes it possible to increase the signal-to-noiseratio in the captured image. This makes it possible to enhance imagequality of the captured image.

In the present embodiment, the arrangement patterns of pixel values inthe image segmentation processing are patterns corresponding to theBayer array to correspond to arrangement of the color filters in theimaging section, which makes it possible to simplify a circuitconfiguration of the image segmentation processing section.

In the present embodiment, in the interpolation processing, theinterpolation methods for generating the map data MIR22 and MG22 are thesame as each other, the interpolation methods for generating the mapdata MIR21 and MR21 are the same as each other, and the interpolationmethods for generating the map data MIR23 and MB23 are the same as eachother, which makes it possible to simplify a circuit configuration ofthe interpolation processing section 93.

In the present embodiment, it is possible to control whether or not toperform the image segmentation processing, the interpolation processing,and the synthesis processing, which makes it possible to enhance imagequality of the captured image.

Modification Example 4-1

Each of the modification examples of the first embodiment may be appliedto the imaging device 5 according to the embodiment described above.Specifically, for example, as in the imaging device 2 (FIG. 21 )according to the modification example of the first embodiment describedabove, the interpolating method in the interpolation processing A2 inthe interpolation processing section 93 may be controlled by performingthe interpolation control processing B1 on the basis of the image mapdata MPRGB included in the image signal DT51.

5. Usage Examples of Imaging Device

FIG. 41 illustrates usage examples of the imaging device 1 and the likeaccording to the embodiments described above. For example, the imagingdevice 1 and the like described above are usable in a variety of casesof sensing light such as visible light, infrared light, ultravioletlight, and X-rays as follows.

-   -   Devices that shoot images for viewing such as digital cameras        and mobile devices having a camera function    -   Devices for traffic use such as onboard sensors that shoot        images of the front, back, surroundings, inside, and so on of an        automobile for safe driving such as automatic stop and for        recognition of a driver's state, monitoring cameras that monitor        traveling vehicles and roads, and distance measuring sensors        that measure vehicle-to-vehicle distance    -   Devices for use in home electrical appliances such as        televisions, refrigerators, and air-conditioners to shoot images        of user's gesture and operate the appliances in accordance with        the gesture    -   Devices for medical care and healthcare use such as endoscopes        and devices that shoot images of blood vessels by receiving        infrared light    -   Devices for security use such as monitoring cameras for crime        prevention and cameras for individual authentication    -   Devices for beauty care use such as skin measuring devices that        shoot images of skin and microscopes that shoot images of scalp    -   Devices for sports use such as action cameras and wearable        cameras for sports applications, etc.    -   Devices for agricultural use such as cameras for monitoring        fields and crops

6. Application Examples

<Example of Application to In-Vivo Information Acquisition System>

Further, the technology (the present technology) according to thepresent disclosure is applicable to various products. For example, thetechnology according to the present disclosure may be applied to anendoscopic surgery system.

FIG. 42 is a block diagram depicting an example of a schematicconfiguration of an in-vivo information acquisition system of a patientusing a capsule type endoscope, to which the technology according to anembodiment of the present disclosure (present technology) can beapplied.

The in-vivo information acquisition system 10001 includes a capsule typeendoscope 10100 and an external controlling apparatus 10200.

The capsule type endoscope 10100 is swallowed by a patient at the timeof inspection. The capsule type endoscope 10100 has an image pickupfunction and a wireless communication function and successively picks upan image of the inside of an organ such as the stomach or an intestine(hereinafter referred to as in-vivo image) at predetermined intervalswhile it moves inside of the organ by peristaltic motion for a period oftime until it is naturally discharged from the patient. Then, thecapsule type endoscope 10100 successively transmits information of thein-vivo image to the external controlling apparatus 10200 outside thebody by wireless transmission.

The external controlling apparatus 10200 integrally controls operationof the in-vivo information acquisition system 10001. Further, theexternal controlling apparatus 10200 receives information of an in-vivoimage transmitted thereto from the capsule type endoscope 10100 andgenerates image data for displaying the in-vivo image on a displayapparatus (not depicted) on the basis of the received information of thein-vivo image.

In the in-vivo information acquisition system 10001, an in-vivo imageimaged a state of the inside of the body of a patient can be acquired atany time in this manner for a period of time until the capsule typeendoscope 10100 is discharged after it is swallowed.

A configuration and functions of the capsule type endoscope 10100 andthe external controlling apparatus 10200 are described in more detailbelow.

The capsule type endoscope 10100 includes a housing 10101 of the capsuletype, in which a light source unit 10111, an image pickup unit 10112, animage processing unit 10113, a wireless communication unit 10114, apower feeding unit 10115, a power supply unit 10116 and a control unit10117 are accommodated.

The light source unit 10111 includes a light source such as, forexample, a light emitting diode (LED) and irradiates light on an imagepickup field-of-view of the image pickup unit 10112.

The image pickup unit 10112 includes an image pickup element and anoptical system including a plurality of lenses provided at a precedingstage to the image pickup element. Reflected light (hereinafter referredto as observation light) of light irradiated on a body tissue which isan observation target is condensed by the optical system and introducedinto the image pickup element. In the image pickup unit 10112, theincident observation light is photoelectrically converted by the imagepickup element, by which an image signal corresponding to theobservation light is generated. The image signal generated by the imagepickup unit 10112 is provided to the image processing unit 10113.

The image processing unit 10113 includes a processor such as a centralprocessing unit (CPU) or a graphics processing unit (GPU) and performsvarious signal processes for an image signal generated by the imagepickup unit 10112. The image processing unit 10113 provides the imagesignal for which the signal processes have been performed thereby as RAWdata to the wireless communication unit 10114.

The wireless communication unit 10114 performs a predetermined processsuch as a modulation process for the image signal for which the signalprocesses have been performed by the image processing unit 10113 andtransmits the resulting image signal to the external controllingapparatus 10200 through an antenna 10114A. Further, the wirelesscommunication unit 10114 receives a control signal relating to drivingcontrol of the capsule type endoscope 10100 from the externalcontrolling apparatus 10200 through the antenna 10114A. The wirelesscommunication unit 10114 provides the control signal received from theexternal controlling apparatus 10200 to the control unit 10117.

The power feeding unit 10115 includes an antenna coil for powerreception, a power regeneration circuit for regenerating electric powerfrom current generated in the antenna coil, a voltage booster circuitand so forth. The power feeding unit 10115 generates electric powerusing the principle of non-contact charging.

The power supply unit 10116 includes a secondary battery and storeselectric power generated by the power feeding unit 10115. In FIG. 42 ,in order to avoid complicated illustration, an arrow mark indicative ofa supply destination of electric power from the power supply unit 10116and so forth are omitted. However, electric power stored in the powersupply unit 10116 is supplied to and can be used to drive the lightsource unit 10111, the image pickup unit 10112, the image processingunit 10113, the wireless communication unit 10114 and the control unit10117.

The control unit 10117 includes a processor such as a CPU and suitablycontrols driving of the light source unit 10111, the image pickup unit10112, the image processing unit 10113, the wireless communication unit10114 and the power feeding unit 10115 in accordance with a controlsignal transmitted thereto from the external controlling apparatus10200.

The external controlling apparatus 10200 includes a processor such as aCPU or a GPU, a microcomputer, a control board or the like in which aprocessor and a storage element such as a memory are mixedlyincorporated. The external controlling apparatus 10200 transmits acontrol signal to the control unit 10117 of the capsule type endoscope10100 through an antenna 10200A to control operation of the capsule typeendoscope 10100. In the capsule type endoscope 10100, an irradiationcondition of light upon an observation target of the light source unit10111 can be changed, for example, in accordance with a control signalfrom the external controlling apparatus 10200. Further, an image pickupcondition (for example, a frame rate, an exposure value or the like ofthe image pickup unit 10112) can be changed in accordance with a controlsignal from the external controlling apparatus 10200. Further, thesubstance of processing by the image processing unit 10113 or acondition for transmitting an image signal from the wirelesscommunication unit 10114 (for example, a transmission interval, atransmission image number or the like) may be changed in accordance witha control signal from the external controlling apparatus 10200.

Further, the external controlling apparatus 10200 performs various imageprocesses for an image signal transmitted thereto from the capsule typeendoscope 10100 to generate image data for displaying a picked upin-vivo image on the display apparatus. As the image processes, varioussignal processes can be performed such as, for example, a developmentprocess (demosaic process), an image quality improving process(bandwidth enhancement process, a super-resolution process, a noisereduction (NR) process and/or image stabilization process) and/or anenlargement process (electronic zooming process). The externalcontrolling apparatus 10200 controls driving of the display apparatus tocause the display apparatus to display a picked up in-vivo image on thebasis of generated image data. Alternatively, the external controllingapparatus 10200 may also control a recording apparatus (not depicted) torecord generated image data or control a printing apparatus (notdepicted) to output generated image data by printing.

An example of the in-vivo information acquisition system to which thetechnology according to the present disclosure may be applied has beendescribed above. The technology according to the present disclosure maybe applied to the image pickup unit 10112 and the image processing unit10113 among the components described above. This makes it possible toenhance image quality of a captured image, which allows the doctor tocomprehend a state of the inside of the body of a patient moreaccurately.

4. Example of Application to Endoscopic Surgery System

The technology (the present technology) according to the presentdisclosure is applicable to various products. For example, thetechnology according to the present disclosure may be applied to anendoscopic surgery system.

FIG. 43 is a view depicting an example of a schematic configuration ofan endoscopic surgery system to which the technology according to anembodiment of the present disclosure (present technology) can beapplied.

In FIG. 43 , a state is illustrated in which a surgeon (medical doctor)11131 is using an endoscopic surgery system 11000 to perform surgery fora patient 11132 on a patient bed 11133. As depicted, the endoscopicsurgery system 11000 includes an endoscope 11100, other surgical tools11110 such as a pneumoperitoneum tube 11111 and an energy device 11112,a supporting arm apparatus 11120 which supports the endoscope 11100thereon, and a cart 11200 on which various apparatus for endoscopicsurgery are mounted.

The endoscope 11100 includes a lens barrel 11101 having a region of apredetermined length from a distal end thereof to be inserted into abody cavity of the patient 11132, and a camera head 11102 connected to aproximal end of the lens barrel 11101. In the example depicted, theendoscope 11100 is depicted which includes as a rigid endoscope havingthe lens barrel 11101 of the hard type. However, the endoscope 11100 mayotherwise be included as a flexible endoscope having the lens barrel11101 of the flexible type.

The lens barrel 11101 has, at a distal end thereof, an opening in whichan objective lens is fitted. A light source apparatus 11203 is connectedto the endoscope 11100 such that light generated by the light sourceapparatus 11203 is introduced to a distal end of the lens barrel 11101by a light guide extending in the inside of the lens barrel 11101 and isirradiated toward an observation target in a body cavity of the patient11132 through the objective lens. It is to be noted that the endoscope11100 may be a forward-viewing endoscope or may be an oblique-viewingendoscope or a side-viewing endoscope.

An optical system and an image pickup element are provided in the insideof the camera head 11102 such that reflected light (observation light)from the observation target is condensed on the image pickup element bythe optical system. The observation light is photo-electricallyconverted by the image pickup element to generate an electric signalcorresponding to the observation light, namely, an image signalcorresponding to an observation image. The image signal is transmittedas RAW data to a CCU 11201.

The CCU 11201 includes a central processing unit (CPU), a graphicsprocessing unit (GPU) or the like and integrally controls operation ofthe endoscope 11100 and a display apparatus 11202. Further, the CCU11201 receives an image signal from the camera head 11102 and performs,for the image signal, various image processes for displaying an imagebased on the image signal such as, for example, a development process(demosaic process).

The display apparatus 11202 displays thereon an image based on an imagesignal, for which the image processes have been performed by the CCU11201, under the control of the CCU 11201.

The light source apparatus 11203 includes a light source such as, forexample, a light emitting diode (LED) and supplies irradiation lightupon imaging of a surgical region to the endoscope 11100.

An inputting apparatus 11204 is an input interface for the endoscopicsurgery system 11000. A user can perform inputting of various kinds ofinformation or instruction inputting to the endoscopic surgery system11000 through the inputting apparatus 11204. For example, the user wouldinput an instruction or a like to change an image pickup condition (typeof irradiation light, magnification, focal distance or the like) by theendoscope 11100.

A treatment tool controlling apparatus 11205 controls driving of theenergy device 11112 for cautery or incision of a tissue, sealing of ablood vessel or the like. A pneumoperitoneum apparatus 11206 feeds gasinto a body cavity of the patient 11132 through the pneumoperitoneumtube 11111 to inflate the body cavity in order to secure the field ofview of the endoscope 11100 and secure the working space for thesurgeon. A recorder 11207 is an apparatus capable of recording variouskinds of information relating to surgery. A printer 11208 is anapparatus capable of printing various kinds of information relating tosurgery in various forms such as a text, an image or a graph.

It is to be noted that the light source apparatus 11203 which suppliesirradiation light when a surgical region is to be imaged to theendoscope 11100 may include a white light source which includes, forexample, an LED, a laser light source or a combination of them. Where awhite light source includes a combination of red, green, and blue (RGB)laser light sources, since the output intensity and the output timingcan be controlled with a high degree of accuracy for each color (eachwavelength), adjustment of the white balance of a picked up image can beperformed by the light source apparatus 11203. Further, in this case, iflaser beams from the respective RGB laser light sources are irradiatedtime-divisionally on an observation target and driving of the imagepickup elements of the camera head 11102 are controlled in synchronismwith the irradiation timings. Then images individually corresponding tothe R, G and B colors can be also picked up time-divisionally. Accordingto this method, a color image can be obtained even if color filters arenot provided for the image pickup element.

Further, the light source apparatus 11203 may be controlled such thatthe intensity of light to be outputted is changed for each predeterminedtime. By controlling driving of the image pickup element of the camerahead 11102 in synchronism with the timing of the change of the intensityof light to acquire images time-divisionally and synthesizing theimages, an image of a high dynamic range free from underexposed blockedup shadows and overexposed highlights can be created.

Further, the light source apparatus 11203 may be configured to supplylight of a predetermined wavelength band ready for special lightobservation. In special light observation, for example, by utilizing thewavelength dependency of absorption of light in a body tissue toirradiate light of a narrow band in comparison with irradiation lightupon ordinary observation (namely, white light), narrow band observation(narrow band imaging) of imaging a predetermined tissue such as a bloodvessel of a superficial portion of the mucous membrane or the like in ahigh contrast is performed. Alternatively, in special light observation,fluorescent observation for obtaining an image from fluorescent lightgenerated by irradiation of excitation light may be performed. Influorescent observation, it is possible to perform observation offluorescent light from a body tissue by irradiating excitation light onthe body tissue (autofluorescence observation) or to obtain afluorescent light image by locally injecting a reagent such asindocyanine green (ICG) into a body tissue and irradiating excitationlight corresponding to a fluorescent light wavelength of the reagentupon the body tissue. The light source apparatus 11203 can be configuredto supply such narrow-band light and/or excitation light suitable forspecial light observation as described above.

FIG. 44 is a block diagram depicting an example of a functionalconfiguration of the camera head 11102 and the CCU 11201 depicted inFIG. 43 .

The camera head 11102 includes a lens unit 11401, an image pickup unit11402, a driving unit 11403, a communication unit 11404 and a camerahead controlling unit 11405. The CCU 11201 includes a communication unit11411, an image processing unit 11412 and a control unit 11413. Thecamera head 11102 and the CCU 11201 are connected for communication toeach other by a transmission cable 11400.

The lens unit 11401 is an optical system, provided at a connectinglocation to the lens barrel 11101. Observation light taken in from adistal end of the lens barrel 11101 is guided to the camera head 11102and introduced into the lens unit 11401. The lens unit 11401 includes acombination of a plurality of lenses including a zoom lens and afocusing lens.

The number of image pickup elements which is included by the imagepickup unit 11402 may be one (single-plate type) or a plural number(multi-plate type). Where the image pickup unit 11402 is configured asthat of the multi-plate type, for example, image signals correspondingto respective R, G and B are generated by the image pickup elements, andthe image signals may be synthesized to obtain a color image. The imagepickup unit 11402 may also be configured so as to have a pair of imagepickup elements for acquiring respective image signals for the right eyeand the left eye ready for three dimensional (3D) display. If 3D displayis performed, then the depth of a living body tissue in a surgicalregion can be comprehended more accurately by the surgeon 11131. It isto be noted that, where the image pickup unit 11402 is configured asthat of stereoscopic type, a plurality of systems of lens units 11401are provided corresponding to the individual image pickup elements.

Further, the image pickup unit 11402 may not necessarily be provided onthe camera head 11102. For example, the image pickup unit 11402 may beprovided immediately behind the objective lens in the inside of the lensbarrel 11101.

The driving unit 11403 includes an actuator and moves the zoom lens andthe focusing lens of the lens unit 11401 by a predetermined distancealong an optical axis under the control of the camera head controllingunit 11405. Consequently, the magnification and the focal point of apicked up image by the image pickup unit 11402 can be adjusted suitably.

The communication unit 11404 includes a communication apparatus fortransmitting and receiving various kinds of information to and from theCCU 11201. The communication unit 11404 transmits an image signalacquired from the image pickup unit 11402 as RAW data to the CCU 11201through the transmission cable 11400.

In addition, the communication unit 11404 receives a control signal forcontrolling driving of the camera head 11102 from the CCU 11201 andsupplies the control signal to the camera head controlling unit 11405.The control signal includes information relating to image pickupconditions such as, for example, information that a frame rate of apicked up image is designated, information that an exposure value uponimage picking up is designated and/or information that a magnificationand a focal point of a picked up image are designated.

It is to be noted that the image pickup conditions such as the framerate, exposure value, magnification or focal point may be designated bythe user or may be set automatically by the control unit 11413 of theCCU 11201 on the basis of an acquired image signal. In the latter case,an auto exposure (AE) function, an auto focus (AF) function and an autowhite balance (AWB) function are incorporated in the endoscope 11100.

The camera head controlling unit 11405 controls driving of the camerahead 11102 on the basis of a control signal from the CCU 11201 receivedthrough the communication unit 11404.

The communication unit 11411 includes a communication apparatus fortransmitting and receiving various kinds of information to and from thecamera head 11102. The communication unit 11411 receives an image signaltransmitted thereto from the camera head 11102 through the transmissioncable 11400.

Further, the communication unit 11411 transmits a control signal forcontrolling driving of the camera head 11102 to the camera head 11102.The image signal and the control signal can be transmitted by electricalcommunication, optical communication or the like.

The image processing unit 11412 performs various image processes for animage signal in the form of RAW data transmitted thereto from the camerahead 11102.

The control unit 11413 performs various kinds of control relating toimage picking up of a surgical region or the like by the endoscope 11100and display of a picked up image obtained by image picking up of thesurgical region or the like. For example, the control unit 11413 createsa control signal for controlling driving of the camera head 11102.

Further, the control unit 11413 controls, on the basis of an imagesignal for which image processes have been performed by the imageprocessing unit 11412, the display apparatus 11202 to display a pickedup image in which the surgical region or the like is imaged. Thereupon,the control unit 11413 may recognize various objects in the picked upimage using various image recognition technologies. For example, thecontrol unit 11413 can recognize a surgical tool such as forceps, aparticular living body region, bleeding, mist when the energy device11112 is used and so forth by detecting the shape, color and so forth ofedges of objects included in a picked up image. The control unit 11413may cause, when it controls the display apparatus 11202 to display apicked up image, various kinds of surgery supporting information to bedisplayed in an overlapping manner with an image of the surgical regionusing a result of the recognition. Where surgery supporting informationis displayed in an overlapping manner and presented to the surgeon11131, the burden on the surgeon 11131 can be reduced and the surgeon11131 can proceed with the surgery with certainty.

The transmission cable 11400 which connects the camera head 11102 andthe CCU 11201 to each other is an electric signal cable ready forcommunication of an electric signal, an optical fiber ready for opticalcommunication or a composite cable ready for both of electrical andoptical communications.

Here, while, in the example depicted, communication is performed bywired communication using the transmission cable 11400, thecommunication between the camera head 11102 and the CCU 11201 may beperformed by wireless communication.

An example of the endoscopic surgery system to which the technologyaccording to the present disclosure may be applied has been describedabove. The technology according to the present disclosure may be appliedto, for example, the image pickup unit 11402 and the image processingunit 11412 among the components described above. This makes it possibleto enhance image quality of a captured image, which allows the doctor tocomprehend a state of the inside of the body of a patient moreaccurately.

It is noted that the endoscopic surgery system has been described hereas an example, but the technology according to the present disclosuremay be additionally applied to, for example, a microscopic surgerysystem or the like.

<Example of Application to Mobile Body>

The technology according to the present disclosure is applicable tovarious products. For example, the technology according to the presentdisclosure may be achieved as a device mounted on any type of mobilebody such as an automobile, an electric vehicle, a hybrid electricvehicle, a motorcycle, a bicycle, a personal mobility, an airplane, adrone, a vessel, a robot, a construction machine, and an agriculturalmachine (tractor).

FIG. 45 is a block diagram depicting an example of schematicconfiguration of a vehicle control system as an example of a mobile bodycontrol system to which the technology according to an embodiment of thepresent disclosure can be applied.

The vehicle control system 12000 includes a plurality of electroniccontrol units connected to each other via a communication network 12001.In the example depicted in FIG. 45 , the vehicle control system 12000includes a driving system control unit 12010, a body system control unit12020, an outside-vehicle information detecting unit 12030, anin-vehicle information detecting unit 12040, and an integrated controlunit 12050. In addition, a microcomputer 12051, a sound/image outputsection 12052, and a vehicle-mounted network interface (I/F) 12053 areillustrated as a functional configuration of the integrated control unit12050.

The driving system control unit 12010 controls the operation of devicesrelated to the driving system of the vehicle in accordance with variouskinds of programs. For example, the driving system control unit 12010functions as a control device for a driving force generating device forgenerating the driving force of the vehicle, such as an internalcombustion engine, a driving motor, or the like, a driving forcetransmitting mechanism for transmitting the driving force to wheels, asteering mechanism for adjusting the steering angle of the vehicle, abraking device for generating the braking force of the vehicle, and thelike.

The body system control unit 12020 controls the operation of variouskinds of devices provided to a vehicle body in accordance with variouskinds of programs. For example, the body system control unit 12020functions as a control device for a keyless entry system, a smart keysystem, a power window device, or various kinds of lamps such as aheadlamp, a backup lamp, a brake lamp, a turn signal, a fog lamp, or thelike. In this case, radio waves transmitted from a mobile device as analternative to a key or signals of various kinds of switches can beinput to the body system control unit 12020. The body system controlunit 12020 receives these input radio waves or signals, and controls adoor lock device, the power window device, the lamps, or the like of thevehicle.

The outside-vehicle information detecting unit 12030 detects informationabout the outside of the vehicle including the vehicle control system12000. For example, the outside-vehicle information detecting unit 12030is connected with an imaging section 12031. The outside-vehicleinformation detecting unit 12030 makes the imaging section 12031 imagean image of the outside of the vehicle, and receives the imaged image.On the basis of the received image, the outside-vehicle informationdetecting unit 12030 may perform processing of detecting an object suchas a human, a vehicle, an obstacle, a sign, a character on a roadsurface, or the like, or processing of detecting a distance thereto.

The imaging section 12031 is an optical sensor that receives light, andwhich outputs an electric signal corresponding to a received lightamount of the light. The imaging section 12031 can output the electricsignal as an image, or can output the electric signal as informationabout a measured distance. In addition, the light received by theimaging section 12031 may be visible light, or may be invisible lightsuch as infrared rays or the like.

The in-vehicle information detecting unit 12040 detects informationabout the inside of the vehicle. The in-vehicle information detectingunit 12040 is, for example, connected with a driver state detectingsection 12041 that detects the state of a driver. The driver statedetecting section 12041, for example, includes a camera that images thedriver. On the basis of detection information input from the driverstate detecting section 12041, the in-vehicle information detecting unit12040 may calculate a degree of fatigue of the driver or a degree ofconcentration of the driver, or may determine whether the driver isdozing.

The microcomputer 12051 can calculate a control target value for thedriving force generating device, the steering mechanism, or the brakingdevice on the basis of the information about the inside or outside ofthe vehicle which information is obtained by the outside-vehicleinformation detecting unit 12030 or the in-vehicle information detectingunit 12040, and output a control command to the driving system controlunit 12010. For example, the microcomputer 12051 can perform cooperativecontrol intended to implement functions of an advanced driver assistancesystem (ADAS) which functions include collision avoidance or shockmitigation for the vehicle, following driving based on a followingdistance, vehicle speed maintaining driving, a warning of collision ofthe vehicle, a warning of deviation of the vehicle from a lane, or thelike.

In addition, the microcomputer 12051 can perform cooperative controlintended for automatic driving, which makes the vehicle to travelautonomously without depending on the operation of the driver, or thelike, by controlling the driving force generating device, the steeringmechanism, the braking device, or the like on the basis of theinformation about the outside or inside of the vehicle which informationis obtained by the outside-vehicle information detecting unit 12030 orthe in-vehicle information detecting unit 12040.

In addition, the microcomputer 12051 can output a control command to thebody system control unit 12020 on the basis of the information about theoutside of the vehicle which information is obtained by theoutside-vehicle information detecting unit 12030. For example, themicrocomputer 12051 can perform cooperative control intended to preventa glare by controlling the headlamp so as to change from a high beam toa low beam, for example, in accordance with the position of a precedingvehicle or an oncoming vehicle detected by the outside-vehicleinformation detecting unit 12030.

The sound/image output section 12052 transmits an output signal of atleast one of a sound and an image to an output device capable ofvisually or auditorily notifying information to an occupant of thevehicle or the outside of the vehicle. In the example of FIG. 45 , anaudio speaker 12061, a display section 12062, and an instrument panel12063 are illustrated as the output device. The display section 12062may, for example, include at least one of an on-board display and ahead-up display.

FIG. 46 is a diagram depicting an example of the installation positionof the imaging section 12031.

In FIG. 46 , the imaging section 12031 includes imaging sections 12101,12102, 12103, 12104, and 12105.

The imaging sections 12101, 12102, 12103, 12104, and 12105 are, forexample, disposed at positions on a front nose, sideview mirrors, a rearbumper, and a back door of the vehicle 12100 as well as a position on anupper portion of a windshield within the interior of the vehicle. Theimaging section 12101 provided to the front nose and the imaging section12105 provided to the upper portion of the windshield within theinterior of the vehicle obtain mainly an image of the front of thevehicle 12100. The imaging sections 12102 and 12103 provided to thesideview mirrors obtain mainly an image of the sides of the vehicle12100. The imaging section 12104 provided to the rear bumper or the backdoor obtains mainly an image of the rear of the vehicle 12100. Theimaging section 12105 provided to the upper portion of the windshieldwithin the interior of the vehicle is used mainly to detect a precedingvehicle, a pedestrian, an obstacle, a signal, a traffic sign, a lane, orthe like.

Incidentally, FIG. 46 depicts an example of photographing ranges of theimaging sections 12101 to 12104. An imaging range 12111 represents theimaging range of the imaging section 12101 provided to the front nose.Imaging ranges 12112 and 12113 respectively represent the imaging rangesof the imaging sections 12102 and 12103 provided to the sideviewmirrors. An imaging range 12114 represents the imaging range of theimaging section 12104 provided to the rear bumper or the back door. Abird's-eye image of the vehicle 12100 as viewed from above is obtainedby superimposing image data imaged by the imaging sections 12101 to12104, for example.

At least one of the imaging sections 12101 to 12104 may have a functionof obtaining distance information. For example, at least one of theimaging sections 12101 to 12104 may be a stereo camera constituted of aplurality of imaging elements, or may be an imaging element havingpixels for phase difference detection.

For example, the microcomputer 12051 can determine a distance to eachthree-dimensional object within the imaging ranges 12111 to 12114 and atemporal change in the distance (relative speed with respect to thevehicle 12100) on the basis of the distance information obtained fromthe imaging sections 12101 to 12104, and thereby extract, as a precedingvehicle, a nearest three-dimensional object in particular that ispresent on a traveling path of the vehicle 12100 and which travels insubstantially the same direction as the vehicle 12100 at a predeterminedspeed (for example, equal to or more than 0 km/hour). Further, themicrocomputer 12051 can set a following distance to be maintained infront of a preceding vehicle in advance, and perform automatic brakecontrol (including following stop control), automatic accelerationcontrol (including following start control), or the like. It is thuspossible to perform cooperative control intended for automatic drivingthat makes the vehicle travel autonomously without depending on theoperation of the driver or the like.

For example, the microcomputer 12051 can classify three-dimensionalobject data on three-dimensional objects into three-dimensional objectdata of a two-wheeled vehicle, a standard-sized vehicle, a large-sizedvehicle, a pedestrian, a utility pole, and other three-dimensionalobjects on the basis of the distance information obtained from theimaging sections 12101 to 12104, extract the classifiedthree-dimensional object data, and use the extracted three-dimensionalobject data for automatic avoidance of an obstacle. For example, themicrocomputer 12051 identifies obstacles around the vehicle 12100 asobstacles that the driver of the vehicle 12100 can recognize visuallyand obstacles that are difficult for the driver of the vehicle 12100 torecognize visually. Then, the microcomputer 12051 determines a collisionrisk indicating a risk of collision with each obstacle. In a situationin which the collision risk is equal to or higher than a set value andthere is thus a possibility of collision, the microcomputer 12051outputs a warning to the driver via the audio speaker 12061 or thedisplay section 12062, and performs forced deceleration or avoidancesteering via the driving system control unit 12010. The microcomputer12051 can thereby assist in driving to avoid collision.

At least one of the imaging sections 12101 to 12104 may be an infraredcamera that detects infrared rays. The microcomputer 12051 can, forexample, recognize a pedestrian by determining whether or not there is apedestrian in imaged images of the imaging sections 12101 to 12104. Suchrecognition of a pedestrian is, for example, performed by a procedure ofextracting characteristic points in the imaged images of the imagingsections 12101 to 12104 as infrared cameras and a procedure ofdetermining whether or not it is the pedestrian by performing patternmatching processing on a series of characteristic points representingthe contour of the object. When the microcomputer 12051 determines thatthere is a pedestrian in the imaged images of the imaging sections 12101to 12104, and thus recognizes the pedestrian, the sound/image outputsection 12052 controls the display section 12062 so that a squarecontour line for emphasis is displayed so as to be superimposed on therecognized pedestrian. The sound/image output section 12052 may alsocontrol the display section 12062 so that an icon or the likerepresenting the pedestrian is displayed at a desired position.

An example of the vehicle control system to which the technologyaccording to the present disclosure may be applied has been describedabove. The technology according to the present disclosure may be appliedto the imaging section 12031 among the components described above. Thismakes it possible to enhance image quality of a captured image, whichallows the vehicle control system 12000 to comprehend, for example, anoutside-vehicle environment more accurately. This makes it possible toperform more accurate driving support and the like.

Although the present technology has been described above referring tosome embodiments, modification examples, and specific applicationexamples thereof, the present technology is not limited to theseembodiments and the like, and may be modified in a variety of ways.

For example, in the respective embodiments described above, the imagingdevice 1 is configured using the imaging section 10 and the imageprocessing section 20, but this is not limitative. Alternatively, forexample, an operation device different from the imaging device 1 mayhave a function of the image processing section 20. In this case, theoperation device is supplied with an image data file includinginformation about the image map data MPR, MPG, and MPB and theconversion gain GC. This allows the operation device to perform theimage segmentation processing A1, the interpolation processing A2, andthe synthesis processing A3 on the basis of the image data file. Theoperation device may include a personal computer that executes an imageprocessing program.

In addition, in the respective embodiments described above, for example,the image processing section 20 controls, on the basis of the conversiongain CG indicated by the gain signal SGAIN, whether or not to performthe image segmentation processing A1, the interpolation processing A2,and the synthesis processing A3, but this is not limitative.Alternatively, for example, the imaging section 10 may determine whetheror not to perform the image segmentation processing A1, theinterpolation processing A2, and the synthesis processing A3, andgenerate a mode signal indicating a result of such determination. Inthis case, it is possible for the image processing section 20 to performan operation in accordance with the mode signal.

It is to be noted that the effects described here are merelyillustrative and non-limiting, and other effects may be included.

It is to be noted that the present technology may be configured asfollows.

(1)

An image processor including:

an image segmentation processing section that is configured to generatea plurality of first map data on the basis of first image map dataincluding a plurality of pixel values, the plurality of first map datahaving arrangement patterns of pixel values different from each otherand including pixel values located at positions different from eachother;

an interpolation processing section that is configured to generate aplurality of second map data corresponding to the plurality of first mapdata by determining a pixel value at a position where no pixel value ispresent in each of the plurality of first map data with use ofinterpolation processing; and

a synthesis processing section that is configured to generate third mapdata by generating, on the basis of pixel values at positionscorresponding to each other in the plurality of second map data, a pixelvalue at a position corresponding to the positions.

(2)

The image processor according to (1), in which the arrangement patternsare checkered patterns.

(3)

The image processor according to (1) or (2), further including aninterpolation controller that is configured to determine a processingmethod in the interpolation processing on the basis of the first imagemap data.

(4)

The image processor according to (3), in which the interpolationcontroller is configured to determine the processing method bydetermining an interpolation direction in the interpolation processingon the basis of the first image map data.

(5)

The image processor according to (3) or (4), in which the interpolationcontroller is configured to determine spatial frequency information onthe basis of the first image map data and determine the processingmethod on the basis of the spatial frequency information.

(6)

The image processor according to any one of (3) to (5), in which theinterpolation controller is configured to generate synthesized map dataon the basis of the first image map data, second image map data, andthird image map data and determine the processing method in theinterpolation processing on the basis of the synthesized map data.

(7)

The image processor according to any one of (1) to (6), in which

the image segmentation processing section is configured to furthergenerate a plurality of fourth map data on the basis of second image mapdata including a plurality of pixel values, the plurality of fourth mapdata having arrangement patterns of pixel values different from eachother and including pixel values located at positions different fromeach other,

the interpolation processing section is configured to generate aplurality of fifth map data corresponding to the plurality of fourth mapdata by determining a pixel value at a position where no pixel value ispresent in each of the plurality of fourth map data with use of theinterpolation processing,

the arrangement patterns of pixel values in the plurality of first mapdata include a first arrangement pattern and a second arrangementpattern, and

the arrangement patterns of pixel values in the plurality of fourth mapdata include the first arrangement pattern and the second arrangementpattern.

(8)

The image processor according to (7), in which an interpolation methodin the interpolation processing on the plurality of first map data issame as an interpolation method in the interpolation processing on theplurality of fourth map data.

(9)

The image processor according to (7) or (8), in which

the plurality of pixel values in the first image map data includes aplurality of pixel values for a first color,

the plurality of pixel values in the second image map data includes aplurality of pixel values for a second color and a plurality of pixelvalues for a third color.

(10)

The image processor according to (7) or (8), in which

the plurality of pixel values in the first image map data includes aplurality of pixel values for a first color, and

the plurality of pixel values in the second image map data includes aplurality of pixel values for a second color, a plurality of pixelvalues for a third color, and a plurality of pixel values for a fourthcolor.

(11)

The image processor according to (7), in which

the synthesis processing section is configured to generate sixth mapdata by generating, on the basis of pixel values at positionscorresponding to each other in the plurality of fifth map data, a pixelvalue at a position corresponding to the positions,

the image segmentation processing section is configured to furthergenerate a plurality of seventh map data on the basis of third image mapdata including a plurality of pixel values, the plurality of seventh mapdata having arrangement patterns of pixel values different from eachother and including pixel values located at positions different fromeach other,

the interpolation processing section is configured to generate aplurality of eighth map data corresponding to the plurality of seventhmap data by determining a pixel value at a position where no pixel valueis present in each of the plurality of seventh map data with use of theinterpolation processing,

the synthesis processing section is configured to generate ninth mapdata by generating, on the basis of pixel values at positionscorresponding to each other in the plurality of eighth map data, a pixelvalue at a position corresponding to the positions, and

the arrangement patterns of pixel values in the plurality of seventh mapdata include the first arrangement pattern and the second arrangementpattern.

(12)

The image processor according to (11), in which an interpolation methodin the interpolation processing on the plurality of first map data issame as an interpolation method in the interpolation processing on theplurality of fourth map data and an interpolation method in theinterpolation processing on the plurality of seventh map data.

(13)

The image processor according to (11) or (12), in which

the plurality of pixel values in the first image map data includes aplurality of pixel values for a first color,

the plurality of pixel values in the second image map data includes aplurality of pixel values for a second color, and

the plurality of pixel values in the third image map data include aplurality of pixel values for a third color.

(14)

The image processor according to any one of (11) to (13), in whichnumber of the plurality of pixel values in the first image map datadifferent from number of the plurality of pixel values in the secondimage map data.

(15)

The image processor according to (14), in which

the plurality of pixel values in the first image map data includes aplurality of pixel values for green, and

two or more pixel values in the first image map data are associated withone pixel value in the second image map data.

(16)

The image processor according to any one of (1) to (5), furtherincluding a generator that generates the first image map data on thebasis of an image signal, in which

the first image map data includes luminance map data.

(17)

The image processor according to any one of (1) to (16), furtherincluding a processing controller that is configured to control whetheror not the image segmentation processing section, the interpolationprocessing section, and the synthesis processing section are to performprocessing.

(18)

The image processor according to (17) further including a processingsection that is configured to perform predetermined signal processing onthe basis of the first image map data or the third map data, in which

the processing controller is configured to cause the processing sectionto perform the predetermined signal processing on the basis of the firstimage map data in a first operation mode, and perform the predeterminedsignal processing on the basis of the third map data in a secondoperation mode.

(19)

The image processor according to (18), in which the processingcontroller is configured to control whether or not the imagesegmentation processing section, the interpolation processing section,and the synthesis processing section are to perform processing on thebasis of a parameter.

(20)

The image processor according to (19), in which

the first image map data is supplied from an imaging section,

the parameter includes a gain value in the imaging section, and

in a case where the gain value is higher than a predetermined gainvalue, the processing controller performs control to cause the imagesegmentation processing section, the interpolation processing section,and the synthesis processing section to perform processing.

(21)

An image processing method including:

image segmentation processing of generating a plurality of first mapdata on the basis of first image map data including a plurality of pixelvalues, the plurality of first map data having arrangement patterns ofpixel values different from each other and including pixel valueslocated at positions different from each other;

interpolation processing of generating a plurality of second map datacorresponding to the plurality of first map data by determining a pixelvalue at a position where no pixel value is present in each of theplurality of first map data with use of interpolation processing; and

synthesis processing of generating third map data by generating, on thebasis of pixel values at positions corresponding to each other in theplurality of second map data, a pixel value at a position correspondingto the positions.

(22)

An imaging device including:

an imaging section that generates first image map data including aplurality of pixel values,

an image segmentation processing section that is configured to generatea plurality of first map data on the basis of the first image map data,the plurality of first map data having arrangement patterns of pixelvalues different from each other and including pixel values located atpositions different from each other;

an interpolation processing section that is configured to generate aplurality of second map data corresponding to the plurality of first mapdata by determining a pixel value at a position where no pixel value ispresent in each of the plurality of first map data with use ofinterpolation processing; and

a synthesis processing section that is configured to generate third mapdata by generating, on the basis of pixel values at positionscorresponding to each other in the plurality of second map data, a pixelvalue at a position corresponding to the positions.

(23)

The imaging device according to (22) in which

the imaging section includes a plurality of pixels arranged inpredetermined color arrangement, and

the arrangement patterns have a pattern corresponding to the colorarrangement.

This application claims the benefit of Japanese Priority PatentApplication JP2018-022143 filed with Japan Patent Office on Feb. 9,2018, the entire contents of which are incorporated herein by reference.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations, and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

What is claimed is:
 1. An image processor, comprising: an imagesegmentation processing section that is configured to generate aplurality of first map data on a basis of first image map data includinga plurality of pixel values, the plurality of first map data havingarrangement patterns of pixel values different from each other andincluding pixel values located at positions different from each other;an interpolation processing section that is configured to generate aplurality of second map data corresponding to the plurality of first mapdata by determining a pixel value at a position where no pixel value ispresent in each of the plurality of first map data with use ofinterpolation processing; and a synthesis processing section that isconfigured to generate third map data by generating, on a basis of pixelvalues at positions corresponding to each other in the plurality ofsecond map data, a pixel value at a position corresponding to thepositions, wherein the image segmentation processing section isconfigured to further generate a plurality of fourth map data on a basisof second image map data including a plurality of pixel values, theplurality of fourth map data having arrangement patterns of pixel valuesdifferent from each other and including pixel values located atpositions different from each other, wherein the interpolationprocessing section is configured to generate a plurality of fifth mapdata corresponding to the plurality of fourth map data by determining apixel value at a position where no pixel value is present in each of theplurality of fourth map data with use of the interpolation processing,wherein the arrangement patterns of pixel values in the plurality offirst map data include a first arrangement pattern and a secondarrangement pattern, and wherein the arrangement patterns of pixelvalues in the plurality of fourth map data include the first arrangementpattern and the second arrangement pattern.
 2. The image processoraccording to claim 1, wherein the arrangement patterns are checkeredpatterns.
 3. The image processor according to claim 1, furthercomprising an interpolation controller that is configured to determine aprocessing method in the interpolation processing on a basis of thefirst image map data.
 4. The image processor according to claim 3,wherein the interpolation controller is configured to determine theprocessing method by determining an interpolation direction in theinterpolation processing on a basis of the first image map data.
 5. Theimage processor according to claim 3, wherein the interpolationcontroller is configured to determine spatial frequency information on abasis of the first image map data and determine the processing method ona basis of the spatial frequency information.
 6. The image processoraccording to claim 3, wherein the interpolation controller is configuredto generate synthesized map data on a basis of the first image map data,second image map data, and third image map data and determine theprocessing method in the interpolation processing on a basis of thesynthesized map data.
 7. The image processor according to claim 1,wherein the plurality of pixel values in the first image map dataincludes a plurality of pixel values for a first color, and wherein theplurality of pixel values in the second image map data includes aplurality of pixel values for a second color and a plurality of pixelvalues for a third color.
 8. The image processor according to claim 1,wherein the plurality of pixel values in the first image map dataincludes a plurality of pixel values for a first color, and wherein theplurality of pixel values in the second image map data includes aplurality of pixel values for a second color, a plurality of pixelvalues for a third color, and a plurality of pixel values for a fourthcolor.
 9. The image processor according to claim 1, wherein thesynthesis processing section is configured to generate sixth map data bygenerating, on a basis of pixel values at positions corresponding toeach other in the plurality of fifth map data, a pixel value at aposition corresponding to the positions, wherein the image segmentationprocessing section is configured to further generate a plurality ofseventh map data on a basis of third image map data including aplurality of pixel values, the plurality of seventh map data havingarrangement patterns of pixel values different from each other andincluding pixel values located at positions different from each other,wherein the interpolation processing section is configured to generate aplurality of eighth map data corresponding to the plurality of seventhmap data by determining a pixel value at a position where no pixel valueis present in each of the plurality of seventh map data with use of theinterpolation processing, wherein the synthesis processing section isconfigured to generate ninth map data by generating, on a basis of pixelvalues at positions corresponding to each other in the plurality ofeighth map data, a pixel value at a position corresponding to thepositions, and wherein the arrangement patterns of pixel values in theplurality of seventh map data include the first arrangement pattern andthe second arrangement pattern.
 10. The image processor according toclaim 9, wherein the plurality of pixel values in the first image mapdata includes a plurality of pixel values for a first color, wherein theplurality of pixel values in the second image map data includes aplurality of pixel values for a second color, and wherein the pluralityof pixel values in the third image map data include a plurality of pixelvalues for a third color.
 11. The image processor according to claim 9,wherein a number of the plurality of pixel values in the first image mapdata are different from a number of the plurality of pixel values in thesecond image map data.
 12. The image processor according to claim 11,wherein the plurality of pixel values in the first image map dataincludes a plurality of pixel values for green, and wherein two or morepixel values in the first image map data are associated with one pixelvalue in the second image map data.
 13. The image processor according toclaim 1, further comprising a generator that generates the first imagemap data on a basis of an image signal, wherein the first image map dataincludes luminance map data.
 14. An image processor, comprising: animage segmentation processing section that is configured to generate aplurality of first map data on a basis of first image map data includinga plurality of pixel values, the plurality of first map data havingarrangement patterns of pixel values different from each other andincluding pixel values located at positions different from each other;an interpolation processing section that is configured to generate aplurality of second map data corresponding to the plurality of first mapdata by determining a pixel value at a position where no pixel value ispresent in each of the plurality of first map data with use ofinterpolation processing; a synthesis processing section that isconfigured to generate third map data by generating, on a basis of pixelvalues at positions corresponding to each other in the plurality ofsecond map data, a pixel value at a position corresponding to thepositions; and a processing controller that is configured to controlwhether or not the image segmentation processing section, theinterpolation processing section, and the synthesis processing sectionare to perform processing.
 15. The image processor according to claim 14further comprising a processing section that is configured to performpredetermined signal processing on a basis of the first image map dataor the third map data, wherein the processing controller is configuredto cause the processing section to perform the predetermined signalprocessing on a basis of the first image map data in a first operationmode, and perform the predetermined signal processing on a basis of thethird map data in a second operation mode.
 16. The image processoraccording to claim 15, wherein the processing controller is configuredto control whether or not the image segmentation processing section, theinterpolation processing section, and the synthesis processing sectionare to perform processing on a basis of a parameter.
 17. The imageprocessor according to claim 16, wherein the first image map data issupplied from an imaging section, wherein the parameter includes a gainvalue in the imaging section, and wherein in a case where the gain valueis higher than a predetermined gain value, the processing controllerperforms control to cause the image segmentation processing section, theinterpolation processing section, and the synthesis processing sectionto perform processing.
 18. An image processing method, comprising: imagesegmentation processing of generating a plurality of first map data on abasis of first image map data including a plurality of pixel values, theplurality of first map data having arrangement patterns of pixel valuesdifferent from each other and including pixel values located atpositions different from each other; interpolation processing ofgenerating a plurality of second map data corresponding to the pluralityof first map data by determining a pixel value at a position where nopixel value is present in each of the plurality of first map data withuse of interpolation processing; and synthesis processing of generatingthird map data by generating, on a basis of pixel values at positionscorresponding to each other in the plurality of second map data, a pixelvalue at a position corresponding to the positions, wherein the imagesegmentation processing further generates a plurality of fourth map dataon a basis of second image map data including a plurality of pixelvalues, the plurality of fourth map data having arrangement patterns ofpixel values different from each other and including pixel valueslocated at positions different from each other, wherein theinterpolation processing generates a plurality of fifth map datacorresponding to the plurality of fourth map data by determining a pixelvalue at a position where no pixel value is present in each of theplurality of fourth map data with use of the interpolation processing,wherein the arrangement patterns of pixel values in the plurality offirst map data include a first arrangement pattern and a secondarrangement pattern, and wherein the arrangement patterns of pixelvalues in the plurality of fourth map data include the first arrangementpattern and the second arrangement pattern.
 19. An imaging device,comprising: an imaging section that generates first image map dataincluding a plurality of pixel values; an image segmentation processingsection that is configured to generate a plurality of first map data ona basis of the first image map data, the plurality of first map datahaving arrangement patterns of pixel values different from each otherand including pixel values located at positions different from eachother; an interpolation processing section that is configured togenerate a plurality of second map data corresponding to the pluralityof first map data by determining a pixel value at a position where nopixel value is present in each of the plurality of first map data withuse of interpolation processing; and a synthesis processing section thatis configured to generate third map data by generating, on a basis ofpixel values at positions corresponding to each other in the pluralityof second map data, a pixel value at a position corresponding to thepositions, wherein the image segmentation processing section isconfigured to further generate a plurality of fourth map data on a basisof second image map data including a plurality of pixel values, theplurality of fourth map data having arrangement patterns of pixel valuesdifferent from each other and including pixel values located atpositions different from each other, wherein the interpolationprocessing section is configured to generate a plurality of fifth mapdata corresponding to the plurality of fourth map data by determining apixel value at a position where no pixel value is present in each of theplurality of fourth map data with use of the interpolation processing,wherein the arrangement patterns of pixel values in the plurality offirst map data include a first arrangement pattern and a secondarrangement pattern, and wherein the arrangement patterns of pixelvalues in the plurality of fourth map data include the first arrangementpattern and the second arrangement pattern.
 20. An image processingmethod, comprising: image segmentation processing of generating aplurality of first map data on a basis of first image map data includinga plurality of pixel values, the plurality of first map data havingarrangement patterns of pixel values different from each other andincluding pixel values located at positions different from each other;interpolation processing of generating a plurality of second map datacorresponding to the plurality of first map data by determining a pixelvalue at a position where no pixel value is present in each of theplurality of first map data with use of interpolation processing;synthesis processing of generating third map data by generating, on abasis of pixel values at positions corresponding to each other in theplurality of second map data, a pixel value at a position correspondingto the positions; and controlling by a processing controller whether ornot the image segmentation processing, the interpolation processing, andthe synthesis processing are to be performed.
 21. An imaging device,comprising: an imaging section that generates first image map dataincluding a plurality of pixel values; an image segmentation processingsection that is configured to generate a plurality of first map data ona basis of the first image map data, the plurality of first map datahaving arrangement patterns of pixel values different from each otherand including pixel values located at positions different from eachother; an interpolation processing section that is configured togenerate a plurality of second map data corresponding to the pluralityof first map data by determining a pixel value at a position where nopixel value is present in each of the plurality of first map data withuse of interpolation processing; a synthesis processing section that isconfigured to generate third map data by generating, on a basis of pixelvalues at positions corresponding to each other in the plurality ofsecond map data, a pixel value at a position corresponding to thepositions; and a processing controller that is configured to controlwhether or not the image segmentation processing section, theinterpolation processing section, and the synthesis processing sectionare to perform processing.